2022
|
 | Aoshima, Koji; Lindmark, Daniel; Servin, Martin: Examining the simulation-to-reality-gap of a wheel loader interacting with deformable terrain. 11th Asia-Pacific Regional Conference of the ISTVS September 26-28, 2022, Harbin, China, 2022. @conference{aoshima2022examining,
title = {Examining the simulation-to-reality-gap of a wheel loader interacting with deformable terrain},
author = {Aoshima, Koji and Lindmark, Daniel and Servin, Martin},
url = {http://umit.cs.umu.se/wl-sim2real/},
year = {2022},
date = {2022-09-01},
booktitle = {11th Asia-Pacific Regional Conference of the ISTVS September 26-28, 2022, Harbin, China},
abstract = {Simulators are essential for developing autonomous control of off-road vehicles and heavy equipment. They allow automatic testing under safe and controllable conditions, and the generation of large amounts of synthetic and annotated training data necessary for deep learning to be applied [1]. Limiting factors are the computational speed and how accurately the simulator reflects the real system. When the deviation is too large, a controller transfers poorly from the simulated to the real environment. On the other hand, a finely resolved simulator easily becomes too computationally intense and slow for running the necessary number of simulations or keeping realtime pace with hardware in the loop. We investigate how well a physics-based simulator can be made to match its physical counterpart, a full-scale wheel loader instrumented with motion and force sensors performing a bucket filling operation [2]. The simulated vehicle is represented as a rigid multibody system with nonsmooth contact and driveline dynamics. The terrain model combines descriptions of the frictional-cohesive soil as a continuous solid and particles, discretized in voxels and discrete elements [3]. Strong and stable force coupling with the equipment is mediated via rigid aggregate bodies capturing the bulk mechanics of the soil. The results include analysis of the agreement between a calibrated simulation model and the field tests, and of how the simulation performance and accuracy depend on spatial and temporal resolution. The system’s degrees of freedom range from hundreds to millions and the simulation speed up to ten times faster than realtime. Furthermore, it is investigated how sensitive a deep learning controller is to variations in the simulator environment parameters.},
keywords = {External},
pubstate = {published},
tppubtype = {conference}
}
Simulators are essential for developing autonomous control of off-road vehicles and heavy equipment. They allow automatic testing under safe and controllable conditions, and the generation of large amounts of synthetic and annotated training data necessary for deep learning to be applied [1]. Limiting factors are the computational speed and how accurately the simulator reflects the real system. When the deviation is too large, a controller transfers poorly from the simulated to the real environment. On the other hand, a finely resolved simulator easily becomes too computationally intense and slow for running the necessary number of simulations or keeping realtime pace with hardware in the loop. We investigate how well a physics-based simulator can be made to match its physical counterpart, a full-scale wheel loader instrumented with motion and force sensors performing a bucket filling operation [2]. The simulated vehicle is represented as a rigid multibody system with nonsmooth contact and driveline dynamics. The terrain model combines descriptions of the frictional-cohesive soil as a continuous solid and particles, discretized in voxels and discrete elements [3]. Strong and stable force coupling with the equipment is mediated via rigid aggregate bodies capturing the bulk mechanics of the soil. The results include analysis of the agreement between a calibrated simulation model and the field tests, and of how the simulation performance and accuracy depend on spatial and temporal resolution. The system’s degrees of freedom range from hundreds to millions and the simulation speed up to ten times faster than realtime. Furthermore, it is investigated how sensitive a deep learning controller is to variations in the simulator environment parameters. |
 | Fälldin, Arvid: Learning by Digging: A Differentiable Prediction Model for an Autonomous Wheel Loader. Department of Physics, Umeå University, 2022. @mastersthesis{Falldin2022,
title = {Learning by Digging: A Differentiable Prediction Model for an Autonomous Wheel Loader},
author = {Arvid Fälldin},
url = {http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-197165
http://www.diva-portal.org/smash/get/diva2:1675538/FULLTEXT01.pdf},
year = {2022},
date = {2022-06-22},
school = {Department of Physics, Umeå University},
abstract = {Wheel loaders are heavy duty machines that are ubiquitous on construction sites and in mines all over the world. Fully autonomous wheel loaders remains an open problem but the industry is hoping that increasing their level of autonomy will help to reduce costs and energy consumption while also increasing workplace safety. Operating a wheel loader efficiently requires dig plans that extend over multiple dig cycles and not just one at a time. This calls for a model that can predict both the performance of a dig action and the resulting shape of the pile. In this thesis project, we use simulations to develop a data-driven artificial neural network model that can predict the outcome of a dig action. The model is able to predict the wheel loader’s productivity with an average error of 7.3%, and the altered shape of the pile with an average relative error of 4.5%. We also show that automatic differentiation techniques can be used to accurately differentiate the model with respect to input. This makes it possible to use gradient-based optimization methods to find the dig action that maximises the performance of the wheel loader.},
keywords = {External},
pubstate = {published},
tppubtype = {mastersthesis}
}
Wheel loaders are heavy duty machines that are ubiquitous on construction sites and in mines all over the world. Fully autonomous wheel loaders remains an open problem but the industry is hoping that increasing their level of autonomy will help to reduce costs and energy consumption while also increasing workplace safety. Operating a wheel loader efficiently requires dig plans that extend over multiple dig cycles and not just one at a time. This calls for a model that can predict both the performance of a dig action and the resulting shape of the pile. In this thesis project, we use simulations to develop a data-driven artificial neural network model that can predict the outcome of a dig action. The model is able to predict the wheel loader’s productivity with an average error of 7.3%, and the altered shape of the pile with an average relative error of 4.5%. We also show that automatic differentiation techniques can be used to accurately differentiate the model with respect to input. This makes it possible to use gradient-based optimization methods to find the dig action that maximises the performance of the wheel loader. |
 | Marklund, Hannes: Expressibility of multiscale physics in deep networks. Department of Physics, Umeå University, 2022. @mastersthesis{itembibentry{marklund2022expressibility},
title = {Expressibility of multiscale physics in deep networks},
author = {Hannes Marklund},
url = {https://umu.diva-portal.org/smash/record.jsf?pid=diva2:1678353},
year = {2022},
date = {2022-06-10},
school = {Department of Physics, Umeå University},
abstract = {Motivated by the successes in the field of deep learning, the scientific community has been increasingly interested in neural networks that are able to reason about physics. As neural networks are universal approximators, they could in theory learn representations that are more efficient than traditional methods whenever improvements are theoretically possible. This thesis, done in collaboration with Algoryx, serves both as a review of the current research in this area and as an experimental investigation of a subset of the proposed methods. We focus on how useful these methods are as textit{learnable simulators} of mechanical systems that are possibly constrained and multiscale. The experimental investigation considers low-dimensional problems with training data generated by either custom numerical integration or by use of the physics engine AGX Dynamics. A good learnable simulator should express some important properties such as being stable, accurate, generalizable, and fast. Importantly, a generalizable simulator must be able to represent reconfigurable environments, requiring a model known as a graph neural network (GNN). The experimental results show that black-box neural networks are limited to approximate physics in the states it has been trained on. The results also suggest that traditional message-passing GNNs have a limited ability to represent more challenging multiscale systems. This is currently the most widely used method to realize GNNs and thus raises concern as there is not much to be gained by investing time into a method with fundamental limitations. },
keywords = {Algoryx},
pubstate = {published},
tppubtype = {mastersthesis}
}
Motivated by the successes in the field of deep learning, the scientific community has been increasingly interested in neural networks that are able to reason about physics. As neural networks are universal approximators, they could in theory learn representations that are more efficient than traditional methods whenever improvements are theoretically possible. This thesis, done in collaboration with Algoryx, serves both as a review of the current research in this area and as an experimental investigation of a subset of the proposed methods. We focus on how useful these methods are as textit{learnable simulators} of mechanical systems that are possibly constrained and multiscale. The experimental investigation considers low-dimensional problems with training data generated by either custom numerical integration or by use of the physics engine AGX Dynamics. A good learnable simulator should express some important properties such as being stable, accurate, generalizable, and fast. Importantly, a generalizable simulator must be able to represent reconfigurable environments, requiring a model known as a graph neural network (GNN). The experimental results show that black-box neural networks are limited to approximate physics in the states it has been trained on. The results also suggest that traditional message-passing GNNs have a limited ability to represent more challenging multiscale systems. This is currently the most widely used method to realize GNNs and thus raises concern as there is not much to be gained by investing time into a method with fundamental limitations. |
 | Özdemir, Erdougan; Coramik, Mustafa: Development of a virtual teaching environment with Algodoo:‘eye’and ‘cactus type light source’models. In: Physics Education, vol. 57, no. 4, pp. 045022, 2022. @article{ozdemir2022development,
title = {Development of a virtual teaching environment with Algodoo:‘eye’and ‘cactus type light source’models},
author = {Özdemir, Erdougan and Coramik, Mustafa},
url = {https://iopscience.iop.org/article/10.1088/1361-6552/ac60b0/pdf},
year = {2022},
date = {2022-05-01},
journal = {Physics Education},
volume = {57},
number = {4},
pages = {045022},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
|
 | Wallin, E.; Wiberg, V.; Vesterlund, F.; Holmgren, J.; Persson, H.; Servin, M.: Learning multiobjective rough terrain traversability. In: Journal of Terramechanics, vol. 102, pp. 17-26, 2022. @article{wallin2022,
title = {Learning multiobjective rough terrain traversability},
author = {Wallin, E. and Wiberg, V. and Vesterlund, F. and Holmgren, J. and Persson, H. and Servin, M.},
url = {https://authors.elsevier.com/sd/article/S0022489822000313
https://www.sciencedirect.com/science/article/pii/S0022489822000313/pdfft?md5=5dda8fdd1e395ea0e205c16deda5aed4&pid=1-s2.0-S0022489822000313-main.pdf
https://arxiv.org/pdf/2203.16354.pdf},
doi = {10.1016/j.jterra.2022.04.002 },
year = {2022},
date = {2022-04-01},
journal = {Journal of Terramechanics},
volume = {102},
pages = {17-26},
abstract = {We present a method that uses high-resolution topography data of rough terrain and ground vehicle simulation to predict traversability. Traversability is expressed as three independent measures: the ability to traverse the terrain at a target speed, energy consumption, and acceleration. The measures are continuous and reflect different objectives for planning that go beyond binary classification. A deep neural network is trained to predict the traversability measures from the local heightmap and target speed. To produce training data, we use an articulated vehicle with wheeled bogie suspensions and procedurally generated terrains. We evaluate the model on laser-scanned forest terrains, previously unseen by the model. The model predicts traversability with an accuracy of 90%. Predictions rely on features from the high-dimensional terrain data that surpass local roughness and slope relative to the heading. Correlations show that the three traversability measures are complementary to each other. With an inference speed 3000 times faster than the ground truth simulation and trivially parallelizable, the model is well suited for traversability analysis and optimal route planning over large areas.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
We present a method that uses high-resolution topography data of rough terrain and ground vehicle simulation to predict traversability. Traversability is expressed as three independent measures: the ability to traverse the terrain at a target speed, energy consumption, and acceleration. The measures are continuous and reflect different objectives for planning that go beyond binary classification. A deep neural network is trained to predict the traversability measures from the local heightmap and target speed. To produce training data, we use an articulated vehicle with wheeled bogie suspensions and procedurally generated terrains. We evaluate the model on laser-scanned forest terrains, previously unseen by the model. The model predicts traversability with an accuracy of 90%. Predictions rely on features from the high-dimensional terrain data that surpass local roughness and slope relative to the heading. Correlations show that the three traversability measures are complementary to each other. With an inference speed 3000 times faster than the ground truth simulation and trivially parallelizable, the model is well suited for traversability analysis and optimal route planning over large areas. |
 | Cao, Muqing; Cao, Kun; Yuan, Shenghai; Nguyen, Thien-Minh; Xie, Lihua: NEPTUNE: Non-Entangling Planning for Multiple Tethered Unmanned Vehicles. In: arxiv:2212.01536, 2022. @article{Cao2022,
title = {NEPTUNE: Non-Entangling Planning for Multiple Tethered Unmanned Vehicles},
author = {Cao, Muqing and Cao, Kun and Yuan, Shenghai and Nguyen, Thien-Minh and Xie, Lihua},
url = {https://arxiv.org/abs/2212.01536
https://arxiv.org/pdf/2212.01536},
year = {2022},
date = {2022-04-01},
journal = {arxiv:2212.01536},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
|
 | Shintani, T.; Saito, Y.; Kiritani, Y.; Ozawa, S.; Obayashi, K.: Applying Model-based Development to Performance Development of Hydraulic Excavators Using 1DCAE. Komatsu Technical Report no. Vol.67 No.174, 2022. @techreport{Shintani2022,
title = {Applying Model-based Development to Performance Development of Hydraulic Excavators Using 1DCAE},
author = {Shintani, T. and Saito, Y. and Kiritani, Y. and Ozawa, S. and Obayashi, K.},
url = {https://www.komatsu.jp/en/-/media/home/aboutus/innovation/technology/techreport/2021/en/174e01.pdf},
year = {2022},
date = {2022-03-31},
number = {Vol.67 No.174},
institution = {Komatsu Technical Report},
abstract = {The requirements for construction machinery are becoming more sophisticated year by year, and the systems are becoming larger and more complex. Komatsu is aiming for efficient development even in large and complicated systems by applying model-based development to vehicle performance development. This paper reports an application example of using 1DCAE for the performance development of a hydraulic excavator, and also introduces examples of further utilization of 1DCAE such as a simulator.},
keywords = {External},
pubstate = {published},
tppubtype = {techreport}
}
The requirements for construction machinery are becoming more sophisticated year by year, and the systems are becoming larger and more complex. Komatsu is aiming for efficient development even in large and complicated systems by applying model-based development to vehicle performance development. This paper reports an application example of using 1DCAE for the performance development of a hydraulic excavator, and also introduces examples of further utilization of 1DCAE such as a simulator. |
 | Song, Ruitao; Ye, Zhixian; Wang, Liyang; He, Tianyi; Zhang, Liangjun: Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC. In: arxiv:2203.08944, 2022. @article{Song2022,
title = {Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC},
author = {Song, Ruitao and Ye, Zhixian and Wang, Liyang and He, Tianyi and Zhang, Liangjun},
url = {https://arxiv.org/abs/2203.08944
https://youtu.be/QbNfS_wZKKA},
doi = {https://doi.org/10.48550/arxiv.2203.08944},
year = {2022},
date = {2022-03-30},
journal = {arxiv:2203.08944},
abstract = {In this paper, we present a systematic approach for high-performance and efficient trajectory tracking control of autonomous wheel loaders. With the nonlinear dynamic model of a wheel loader, nonlinear model predictive control (MPC) is used in offline trajectory planning to obtain a high-performance state-control trajectory while satisfying the state and control constraints. In tracking control, the nonlinear model is embedded into a Linear Parameter Varying (LPV) model and the LPV-MPC strategy is used to achieve fast online computation and good tracking performance. To demonstrate the effectiveness and the advantages of the LPV-MPC, we test and compare three model predictive control strategies in the high-fidelity simulation environment. With the planned trajectory, three tracking control strategies LPV-MPC, nonlinear MPC, and LTI-MPC are simulated and compared in the perspectives of computational burden and tracking performance. The LPV-MPC can achieve better performance than conventional LTI-MPC because more accurate nominal system dynamics are captured in the LPV model. In addition, LPV-MPC achieves slightly worse tracking performance but tremendously improved computational efficiency than nonlinear MPC. A video with loading cycles completed by our autonomous wheel loader in the simulation environment can be found here: this https URL.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a systematic approach for high-performance and efficient trajectory tracking control of autonomous wheel loaders. With the nonlinear dynamic model of a wheel loader, nonlinear model predictive control (MPC) is used in offline trajectory planning to obtain a high-performance state-control trajectory while satisfying the state and control constraints. In tracking control, the nonlinear model is embedded into a Linear Parameter Varying (LPV) model and the LPV-MPC strategy is used to achieve fast online computation and good tracking performance. To demonstrate the effectiveness and the advantages of the LPV-MPC, we test and compare three model predictive control strategies in the high-fidelity simulation environment. With the planned trajectory, three tracking control strategies LPV-MPC, nonlinear MPC, and LTI-MPC are simulated and compared in the perspectives of computational burden and tracking performance. The LPV-MPC can achieve better performance than conventional LTI-MPC because more accurate nominal system dynamics are captured in the LPV model. In addition, LPV-MPC achieves slightly worse tracking performance but tremendously improved computational efficiency than nonlinear MPC. A video with loading cycles completed by our autonomous wheel loader in the simulation environment can be found here: this https URL. |
 | Despaigne, Henrik: Fibre-based preconditioner for granular matter simulation. Department of Computing Science, Umeå University, 2022. @mastersthesis{Despaigne2022,
title = {Fibre-based preconditioner for granular matter simulation},
author = {Henrik Despaigne},
url = {https://www.diva-portal.org/smash/record.jsf?pid=diva2:1638655
https://www.diva-portal.org/smash/get/diva2:1638655/FULLTEXT01.pdf},
year = {2022},
date = {2022-02-18},
school = {Department of Computing Science, Umeå University},
abstract = {Simulating granular media at large scales is hard to do because of ill-conditioning of the associated linear systems and the ineffectiveness of available iterative methods. One common way to improve iterative methods is to use a preconditioner which involves finding a good approximation of a linear system A. A good preconditioner will improve the condition number of A. If a linear system has a set of large eigenvalues of comparable magnitude, and the rest of the eigenvalues are small, so that the gap between the set of large eigenvalues and the small ones is large, the ill-conditioning caused by the small eigenvalues will not appear in the early iterations. We investigate a new fibre-based preconditioner that involves finding chains of contacting particles along the particles of a granular medium and reordering the system, which leads to a diagonal preconditioner. We show its effects on the relative residual and error of the velocity on linear systems where the ill-conditioning is caused by a big gap between a set of large eigenvalues and small eigenvalues for three differentiterative methods: Uzawa, the Conjugate Residual (CR) and the Minimum ResidualMethod (MINRES).},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {mastersthesis}
}
Simulating granular media at large scales is hard to do because of ill-conditioning of the associated linear systems and the ineffectiveness of available iterative methods. One common way to improve iterative methods is to use a preconditioner which involves finding a good approximation of a linear system A. A good preconditioner will improve the condition number of A. If a linear system has a set of large eigenvalues of comparable magnitude, and the rest of the eigenvalues are small, so that the gap between the set of large eigenvalues and the small ones is large, the ill-conditioning caused by the small eigenvalues will not appear in the early iterations. We investigate a new fibre-based preconditioner that involves finding chains of contacting particles along the particles of a granular medium and reordering the system, which leads to a diagonal preconditioner. We show its effects on the relative residual and error of the velocity on linear systems where the ill-conditioning is caused by a big gap between a set of large eigenvalues and small eigenvalues for three differentiterative methods: Uzawa, the Conjugate Residual (CR) and the Minimum ResidualMethod (MINRES). |
 | Ürek, Handan; Çoramik, Mustafa: A Suggestion and Evaluation of a STEM Activity about Friction Coefficient for Pre-Service Science Teachers. In: Journal of Computer and Education Research, vol. 10, no. 19, pp. 202 - 235, 2022. @article{Urek2022,
title = {A Suggestion and Evaluation of a STEM Activity about Friction Coefficient for Pre-Service Science Teachers},
author = {Handan Ürek and Mustafa Çoramik},
url = {https://dergipark.org.tr/en/pub/jcer/issue/69325/1063301
https://dergipark.org.tr/en/download/article-file/2214073},
doi = {10.18009/jcer.1063301},
year = {2022},
date = {2022-01-02},
journal = {Journal of Computer and Education Research},
volume = {10},
number = {19},
pages = {202 - 235},
abstract = {In this study, the process of developing and evaluating a STEM activity which can be implemented during Science Teaching Laboratory Practice course in accordance with the 5E Model related to the concept of friction coefficient was addressed. The implementation of the activity was conducted in the form of a case study with the participation of 16 third year pre-service science teachers. Student journal forms and worksheets were utilized for the evaluation of the activity. As a result, it was determined that planned activity could be successfully applied to the pre-service teachers during the weekly course hours of Science Teaching Laboratory Practice course. In addition, positive feedbacks were obtained from pre-service teachers’ evaluation for the activity. It is believed that such studies, which establish connections between science and different disciplines, can contribute to the training of qualified science teachers and that such studies should be given more space in science teacher education.
},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
In this study, the process of developing and evaluating a STEM activity which can be implemented during Science Teaching Laboratory Practice course in accordance with the 5E Model related to the concept of friction coefficient was addressed. The implementation of the activity was conducted in the form of a case study with the participation of 16 third year pre-service science teachers. Student journal forms and worksheets were utilized for the evaluation of the activity. As a result, it was determined that planned activity could be successfully applied to the pre-service teachers during the weekly course hours of Science Teaching Laboratory Practice course. In addition, positive feedbacks were obtained from pre-service teachers’ evaluation for the activity. It is believed that such studies, which establish connections between science and different disciplines, can contribute to the training of qualified science teachers and that such studies should be given more space in science teacher education.
|
 | Wiberg, Viktor; Wallin, Erik; Servin, Martin; Nordfjell, Tomas: Control of rough terrain vehicles using deep reinforcement learning. In: IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 390-397, 2022. @article{wiberg2021b,
title = {Control of rough terrain vehicles using deep reinforcement learning},
author = {Viktor Wiberg and Erik Wallin and Martin Servin and Tomas Nordfjell},
url = {https://arxiv.org/abs/2107.01867
http://umit.cs.umu.se/control_terrain/},
doi = {https://ieeexplore.ieee.org/document/9611016},
year = {2022},
date = {2022-01-01},
journal = {IEEE Robotics and Automation Letters},
volume = {7},
number = {1},
pages = {390-397},
abstract = {We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27°, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.
},
howpublished = { arXiv:2107.01867},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27°, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.
|
 | Mustafa, Erdoğan Özdemir Coramik: Development of a virtual teaching environment with Algodoo: eye and cactus type light source models. In: Physics Education, vol. 57, no. 4, pp. 045022, 2022. @article{Özdemir2022,
title = {Development of a virtual teaching environment with Algodoo: eye and cactus type light source models},
author = {Erdoğan Özdemir
Coramik Mustafa},
url = {https://iopscience.iop.org/article/10.1088/1361-6552/ac60b0
https://iopscience.iop.org/article/10.1088/1361-6552/ac60b0/pdf?casa_token=V8xrPx7vhDAAAAAA:rPLZ6ro88reQ4VsHcF3r17FcMFZS5zgzLEr7VJMegEl3W8ni7Xy9hDyRQKmWdtQBQqQDDVQR},
doi = {10.1088/1361-6552/ac60b0},
year = {2022},
date = {2022-01-01},
journal = {Physics Education},
volume = {57},
number = {4},
pages = {045022},
abstract = {It is often necessary to enrich the teaching environment in order for students to learn optics in depth and to interpret the real optical situations with the information they have learned. In this study, a virtual teaching environment was developed using by Algodoo, a 2D simulation software. An eye model was created in order to explain the formation of the image in the eye in the teaching environment. Also, a cactus type light source model has also been developed to demonstrate that the image can be created in optics by using rays other than special rays. In order to show the success of the virtual teaching environment in modelling, previously known situations that the students had difficulty in understanding were simulated. Finally, some suggestions were made about the use of the virtual teaching environment. },
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
It is often necessary to enrich the teaching environment in order for students to learn optics in depth and to interpret the real optical situations with the information they have learned. In this study, a virtual teaching environment was developed using by Algodoo, a 2D simulation software. An eye model was created in order to explain the formation of the image in the eye in the teaching environment. Also, a cactus type light source model has also been developed to demonstrate that the image can be created in optics by using rays other than special rays. In order to show the success of the virtual teaching environment in modelling, previously known situations that the students had difficulty in understanding were simulated. Finally, some suggestions were made about the use of the virtual teaching environment. |
2021
|
 | Andersson, Jennifer; Servin, Martin: Predicting Gripability Heatmaps using Conditional GANs. Department of Physics. Umeå University Technical report, no. diva2:1622970, 2021. @techreport{Andersson2021b,
title = {Predicting Gripability Heatmaps using Conditional GANs},
author = {Jennifer Andersson and Martin Servin},
url = {https://www.diva-portal.org/smash/record.jsf?pid=diva2:1622970
https://www.diva-portal.org/smash/get/diva2:1622970/FULLTEXT01.pdf},
year = {2021},
date = {2021-12-15},
number = {diva2:1622970},
institution = {Department of Physics. Umeå University},
abstract = {The feasibility of using conditional GANs (Generative Adversarial Networks) to predict gripability in log piles is investigated. This is done by posing gripability heatmap prediction from RGB-D data as an image-to-image translation problem. Conditional GANs have previously achieved impressive results on several image-to-image translation tasks predicting physical properties and adding details not present in the input images. Here, piles of logs modelled as sticks or rods are generated in simulation, and groundtruth gripability maps are created using a simple algorithm streamlining the datacollection process. A modified SSIM (Structural Similarity Index) is used to evaluate the quality of the gripability heatmap predictions. The results indicate promising model performance on several different datasets and heatmap designs, including using base plane textures from a real forest production site to add realistic noise in the RGB data. Including a depth channel in the input data is shown to increase performance compared to using pure RGB data. The implementation is based on the general Pix2Pix network developed by Isola et al. in 2017. However, there is potential to increase performance and model generalization, and the adoption of more advanced loss functions and network architectures are suggested. Next steps include using terrains reconstructed from highdensity laser scans in physics-based simulation for data generation. A more in-depth discussion regarding the level of sophistication required in the gripability heatmaps should also be carried out, along with discussions regarding other specifications that will be required for future deployment. This will enable derivation of a tailored gripability metric for ground-truth heatmap generation, and method evaluation on less ideal data.},
type = {Technical report},
keywords = {External},
pubstate = {published},
tppubtype = {techreport}
}
The feasibility of using conditional GANs (Generative Adversarial Networks) to predict gripability in log piles is investigated. This is done by posing gripability heatmap prediction from RGB-D data as an image-to-image translation problem. Conditional GANs have previously achieved impressive results on several image-to-image translation tasks predicting physical properties and adding details not present in the input images. Here, piles of logs modelled as sticks or rods are generated in simulation, and groundtruth gripability maps are created using a simple algorithm streamlining the datacollection process. A modified SSIM (Structural Similarity Index) is used to evaluate the quality of the gripability heatmap predictions. The results indicate promising model performance on several different datasets and heatmap designs, including using base plane textures from a real forest production site to add realistic noise in the RGB data. Including a depth channel in the input data is shown to increase performance compared to using pure RGB data. The implementation is based on the general Pix2Pix network developed by Isola et al. in 2017. However, there is potential to increase performance and model generalization, and the adoption of more advanced loss functions and network architectures are suggested. Next steps include using terrains reconstructed from highdensity laser scans in physics-based simulation for data generation. A more in-depth discussion regarding the level of sophistication required in the gripability heatmaps should also be carried out, along with discussions regarding other specifications that will be required for future deployment. This will enable derivation of a tailored gripability metric for ground-truth heatmap generation, and method evaluation on less ideal data. |
 | Aoshima, Koji; Servin, Martin; Wadbro, Eddie: Simulation-Based Optimization of High-Performance Wheel Loading. Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC), International Association for Automation and Robotics in Construction (IAARC), 2021, ISBN: 978-952-69524-1-3. @conference{Aoshima2021,
title = {Simulation-Based Optimization of High-Performance Wheel Loading},
author = {Koji Aoshima and Martin Servin and Eddie Wadbro},
editor = {Feng, Chen and Linner, Thomas and Brilakis, Ioannis and Castro, Daniel and Chen, Po-Han and Cho, Yong and Du, Jing and Ergan, Semiha and Garcia de Soto, Borja and Gaparík, Jozef and Habbal, Firas and Hammad, Amin and Iturralde, Kepa and Bock, Thomas and Kwon, Soonwook and Lafhaj, Zoubeir and Li, Nan and Liang, Ci-Jyun and Mantha, Bharadwaj and Ng, Ming Shan and Hall, Daniel and Pan, Mi and Pan, Wei and Rahimian, Farzad and Raphael, Benny and Sattineni, Anoop and Schlette, Christian and Shabtai, Isaac and Shen, Xuesong and Tang, Pingbo and Teizer, Jochen and Turkan, Yelda and Valero, Enrique and Zhu, Zhenhua},
url = {https://www.iaarc.org/publications/2021_proceedings_of_the_38th_isarc/simulation_based_optimization_of_high_performance_wheel_loading.html
https://arxiv.org/abs/2107.14615
http://umit.cs.umu.se/hp_loading/},
doi = {10.22260/ISARC2021/009310.22260/ISARC2021/0093},
isbn = {978-952-69524-1-3},
year = {2021},
date = {2021-08-02},
booktitle = {Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)},
pages = {688-695},
publisher = {International Association for Automation and Robotics in Construction (IAARC)},
abstract = {Having smart and autonomous earthmoving in mind, we explore high-performance wheel loading in a simulated environment. This paper introduces a wheel loader simulator that combines contacting 3D multibody dynamics with a hybrid continuum-particle terrain model, supporting realistic digging forces and soil displacements at real-time performance. A total of 270,000 simulations are run with different loading actions, pile slopes, and soil to analyze how they affect the loading performance. The results suggest that the preferred digging actions should preserve and exploit a steep pile slope. High digging speed favors high productivity, while energy-efficient loading requires a lower dig speed.},
howpublished = {38th International Symposium on Automation and Robotics in Construction (ISARC), Dubai, UAE (2021). arXiv:2107.14615 },
keywords = {Algoryx, External},
pubstate = {published},
tppubtype = {conference}
}
Having smart and autonomous earthmoving in mind, we explore high-performance wheel loading in a simulated environment. This paper introduces a wheel loader simulator that combines contacting 3D multibody dynamics with a hybrid continuum-particle terrain model, supporting realistic digging forces and soil displacements at real-time performance. A total of 270,000 simulations are run with different loading actions, pile slopes, and soil to analyze how they affect the loading performance. The results suggest that the preferred digging actions should preserve and exploit a steep pile slope. High digging speed favors high productivity, while energy-efficient loading requires a lower dig speed. |
 | Selby, Nicholas S; Asada, Harry H: Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation. In: IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 6297 - 6304, 2021. @article{selby2021learning,
title = {Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation},
author = {Nicholas S Selby and Harry H Asada},
url = {https://arxiv.org/abs/2104.02004
https://arxiv.org/pdf/2104.02004
},
doi = {https://doi.org/10.1109/LRA.2021.3092256},
year = {2021},
date = {2021-06-01},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {4},
pages = {6297 - 6304},
abstract = {Effective and causal observable functions for low-order lifting linearization of nonlinear controlled systems are learned from data by using neural networks. While Koopman operator theory allows us to represent a nonlinear system as a linear system in an infinite-dimensional space of observables, exact linearization is guaranteed only for autonomous systems with no input, and finding effective observable functions for approximation with a low-order linear system remains an open question. Dual-Faceted Linearization uses a set of effective observables for low-order lifting linearization, but the method requires knowledge of the physical structure of the nonlinear system. Here, a data-driven method is presented for generating a set of nonlinear observable functions that can accurately approximate a nonlinear control system to a low-order linear control system. A caveat in using data of measured variables as observables is that the measured variables may contain input to the system, which incurs a causality contradiction when lifting the system, i.e., taking derivatives of the observables. The current work presents a method for eliminating such anti-causal components of the observables and lifting the system using only causal observables. The method is applied to excavation automation, a complex nonlinear dynamical system, to obtain a low-order lifted linear model for control design.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
Effective and causal observable functions for low-order lifting linearization of nonlinear controlled systems are learned from data by using neural networks. While Koopman operator theory allows us to represent a nonlinear system as a linear system in an infinite-dimensional space of observables, exact linearization is guaranteed only for autonomous systems with no input, and finding effective observable functions for approximation with a low-order linear system remains an open question. Dual-Faceted Linearization uses a set of effective observables for low-order lifting linearization, but the method requires knowledge of the physical structure of the nonlinear system. Here, a data-driven method is presented for generating a set of nonlinear observable functions that can accurately approximate a nonlinear control system to a low-order linear control system. A caveat in using data of measured variables as observables is that the measured variables may contain input to the system, which incurs a causality contradiction when lifting the system, i.e., taking derivatives of the observables. The current work presents a method for eliminating such anti-causal components of the observables and lifting the system using only causal observables. The method is applied to excavation automation, a complex nonlinear dynamical system, to obtain a low-order lifted linear model for control design. |
 | Susanti, D; Yaqiina, MH; Maulana, S: Newton’s Gravity Interactive Simulation to Improve 10th-Grade Students’ Learning Outcome. Journal of Physics: Conference Series, vol. 2377, no. 1, IOP Publishing, 2021. @conference{susanti2022newton,
title = {Newton’s Gravity Interactive Simulation to Improve 10th-Grade Students’ Learning Outcome},
author = {Susanti, D and Yaqiina, MH and Maulana, S},
url = {https://iopscience.iop.org/article/10.1088/1742-6596/2377/1/012091/meta},
year = {2021},
date = {2021-06-01},
booktitle = {Journal of Physics: Conference Series},
volume = {2377},
number = {1},
pages = {012091},
publisher = {IOP Publishing},
keywords = {External},
pubstate = {published},
tppubtype = {conference}
}
|
 | Shima, Daichi; Furukawa, Tomoyuki; Aoba, Ryuma; Ohashi, Ayato; Tsuruno, Kota; Naruse, Keitaro: Team Activity of Robot Competition of Simulated Robot in World Robot Summit 2020. In: SHS Web of Conferences, pp. 04016, EDP Sciences 2021, ISSN: 2261-2424. @inproceedings{shima2021team,
title = {Team Activity of Robot Competition of Simulated Robot in World Robot Summit 2020},
author = {Daichi Shima and Tomoyuki Furukawa and Ryuma Aoba and Ayato Ohashi and Kota Tsuruno and Keitaro Naruse},
url = {https://www.shs-conferences.org/articles/shsconf/abs/2021/13/shsconf_etltc2021_04016/shsconf_etltc2021_04016.html
https://www.shs-conferences.org/articles/shsconf/pdf/2021/13/shsconf_etltc2021_04016.pdf
},
issn = {2261-2424},
year = {2021},
date = {2021-05-08},
booktitle = {SHS Web of Conferences},
volume = {102},
pages = {04016},
organization = {EDP Sciences},
abstract = {World Robot Summit (WRS) has several robot competitions, and we will participate it in the infrastructure and disaster response category. Participating teams develop their robot system by teleoperation and/or autonomous operation and run it in a set of courses modelling and simplifying disaster responding situations. The authors will attend the challenge of the tunnel disaster response and recovery, in which we are requested to achieve an investigation and rescue scenario of a tunnel fire with simulated robots. As preparation, we develop simulated robot models and corresponding software as a team. In this article, we report out activity to the robot competition and student’s project-based learning by joining it.},
keywords = {External},
pubstate = {published},
tppubtype = {inproceedings}
}
World Robot Summit (WRS) has several robot competitions, and we will participate it in the infrastructure and disaster response category. Participating teams develop their robot system by teleoperation and/or autonomous operation and run it in a set of courses modelling and simplifying disaster responding situations. The authors will attend the challenge of the tunnel disaster response and recovery, in which we are requested to achieve an investigation and rescue scenario of a tunnel fire with simulated robots. As preparation, we develop simulated robot models and corresponding software as a team. In this article, we report out activity to the robot competition and student’s project-based learning by joining it. |
 | Servin, Martin; Westerlund, Folke; Wallin, Erik: Digital twins with distributed particle simulation for mine-to-mill material tracking. In: Minerals, vol. 11, no. 5, pp. 524, 2021. @article{Servin2021b,
title = {Digital twins with distributed particle simulation for mine-to-mill material tracking},
author = {Martin Servin and Folke Westerlund and Erik Wallin},
url = {https://www.mdpi.com/2075-163X/11/5/524
https://arxiv.org/pdf/2104.09111.pdf},
doi = {https://doi.org/10.3390/min11050524},
year = {2021},
date = {2021-04-19},
journal = {Minerals},
volume = {11},
number = {5},
pages = {524},
abstract = {Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries these systems are chains of pro- cesses with complex interplay between the equipment, control, and the processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explore a material-oriented approach to digital twins with a particle representa- tion of the granular media. In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and, mineral properties. Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics, and simulation models at locations where no real sensors can see. The particle-based representation enables material tracking along the chain of processes. Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material. This makes it possible to better learn material properties from process observations, and to predict the effect on downstream processes. We test the technique on a mining simulator and demonstrate analysis that can be performed using data from cross-system material tracking.
},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries these systems are chains of pro- cesses with complex interplay between the equipment, control, and the processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explore a material-oriented approach to digital twins with a particle representa- tion of the granular media. In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and, mineral properties. Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics, and simulation models at locations where no real sensors can see. The particle-based representation enables material tracking along the chain of processes. Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material. This makes it possible to better learn material properties from process observations, and to predict the effect on downstream processes. We test the technique on a mining simulator and demonstrate analysis that can be performed using data from cross-system material tracking.
|
 | Servin, Martin; Götz, Holger; Berglund, Tomas; Wallin, Erik: Towards a graph neural network solver for granular dynamics. VII International Conference on Particle-Based Methods (PARTICLES 2021), 2021. @conference{servintowards,
title = {Towards a graph neural network solver for granular dynamics},
author = {Martin Servin and Holger Götz and Tomas Berglund and Erik Wallin},
url = {https://www.researchgate.net/profile/Martin-Servin/publication/350089857_Towards_a_graph_neural_network_solver_for_granular_dynamics/links/6050749892851cd8ce445ca6/Towards-a-graph-neural-network-solver-for-granular-dynamics.pdf},
year = {2021},
date = {2021-03-01},
booktitle = {VII International Conference on Particle-Based Methods (PARTICLES 2021)},
abstract = {The discrete element method (DEM) is a versatile but computationally intensive method for granular dynamics simulation. We investigate the possibility of accelerating DEM simulations using graph neural networks (GNN), which automatically support variable connectivity between particles. This approach was recently found promising for particle-based simulation of complex fluids [1]. We start from a time-implicit, or nonsmooth, DEM [2], where the computational bottleneck is the process of solving a mixed linear complementarity problem (MLCP) to obtain the contact forces and particle velocity update. This solve step is substituted by a GNN, trained to predict the MLCP solution. Following [1], we employ an encoder-process-decoder structure for the GNN. The particle and connectivity data is encoded in an input graph with particle mass, external force, and previous velocity as node attributes, and contact overlap, normal, and tangent vectors as edge attributes. The sought solution is represented in the output graph with the updated particle velocities as node attributes and the contact forces as edge attributes. In the intermediate processing step, the input graph is converted to a latent graph, which is then advanced with a fixed number of message passing steps involving a multilayer perceptron neural network for updating the edge and node values. The output graph, with the approximate solution to the MLCP, is finally computed by decoding the last processed latent graph. Both a supervised and unsupervised method are tested for training the network on granular simulation of particles in a rotating or static drum. AGX Dynamics [3] is used for running the simulations, and Pytorch [4] in combination with the Deep Graph Library [5] for the learning. The supervised model learns from ground truth MLCP solutions, computed using a projected Gauss-Seidel (PGS) solver, sampled from 1200 simulations involving 50-150 particles. The unsupervised model learns to minimize a loss function derived from the MLCP residual function using particle configurations extracted from the same simulations but ignoring the approximate solution from the PGS solver. The simulation samples are split into training data (80%), validation data (10%), and test data (10%). Network hyperparameter optimization is performed. The supervised GNN solver reaches an error level of 1% for the contact forces and 0.01% on the particle velocities for a static drum. For a rotating drum, the respective errors are 10% and 1%. The unsupervised GNN solver reaches 1% velocity errors, 5% normal forces errors, but it has significant problems with predicting the friction forces. The latter is presumably because of the discontinuous loss function that follows from the Coulomb friction law and therefore we explore regularization of it. Finally, we discuss the potential scalability and performance for large particle systems.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {conference}
}
The discrete element method (DEM) is a versatile but computationally intensive method for granular dynamics simulation. We investigate the possibility of accelerating DEM simulations using graph neural networks (GNN), which automatically support variable connectivity between particles. This approach was recently found promising for particle-based simulation of complex fluids [1]. We start from a time-implicit, or nonsmooth, DEM [2], where the computational bottleneck is the process of solving a mixed linear complementarity problem (MLCP) to obtain the contact forces and particle velocity update. This solve step is substituted by a GNN, trained to predict the MLCP solution. Following [1], we employ an encoder-process-decoder structure for the GNN. The particle and connectivity data is encoded in an input graph with particle mass, external force, and previous velocity as node attributes, and contact overlap, normal, and tangent vectors as edge attributes. The sought solution is represented in the output graph with the updated particle velocities as node attributes and the contact forces as edge attributes. In the intermediate processing step, the input graph is converted to a latent graph, which is then advanced with a fixed number of message passing steps involving a multilayer perceptron neural network for updating the edge and node values. The output graph, with the approximate solution to the MLCP, is finally computed by decoding the last processed latent graph. Both a supervised and unsupervised method are tested for training the network on granular simulation of particles in a rotating or static drum. AGX Dynamics [3] is used for running the simulations, and Pytorch [4] in combination with the Deep Graph Library [5] for the learning. The supervised model learns from ground truth MLCP solutions, computed using a projected Gauss-Seidel (PGS) solver, sampled from 1200 simulations involving 50-150 particles. The unsupervised model learns to minimize a loss function derived from the MLCP residual function using particle configurations extracted from the same simulations but ignoring the approximate solution from the PGS solver. The simulation samples are split into training data (80%), validation data (10%), and test data (10%). Network hyperparameter optimization is performed. The supervised GNN solver reaches an error level of 1% for the contact forces and 0.01% on the particle velocities for a static drum. For a rotating drum, the respective errors are 10% and 1%. The unsupervised GNN solver reaches 1% velocity errors, 5% normal forces errors, but it has significant problems with predicting the friction forces. The latter is presumably because of the discontinuous loss function that follows from the Coulomb friction law and therefore we explore regularization of it. Finally, we discuss the potential scalability and performance for large particle systems. |
 | Wallin, Erik; Servin, Martin: Data-driven model order reduction for granular media. In: Computational Particle Mechanics, pp. 1–14, 2021. @article{wallin2021data,
title = {Data-driven model order reduction for granular media},
author = {Erik Wallin and Martin Servin},
url = {http://umit.cs.umu.se/ddgranular/
https://doi.org/10.1007/s40571-020-00387-6
https://arxiv.org/pdf/2004.03349
https://arxiv.org/abs/2004.03349
https://youtu.be/YjwP9baTm-c?list=TLGGYuzbbp4IBtcxOTA0MjAyMQ},
year = {2021},
date = {2021-01-01},
journal = {Computational Particle Mechanics},
pages = {1--14},
publisher = {Springer},
abstract = {We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distribution and system control signals. Rapid model inference of particle velocities replaces the intense process of computing contact forces and velocity updates. In coupled DEM and multibody system simulation the predictor model can be trained to output the interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the media in domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particle motion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regression predictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90% and 65%, respectively, and the speed-up range between 10 and 60},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distribution and system control signals. Rapid model inference of particle velocities replaces the intense process of computing contact forces and velocity updates. In coupled DEM and multibody system simulation the predictor model can be trained to output the interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the media in domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particle motion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regression predictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90% and 65%, respectively, and the speed-up range between 10 and 60 |
 | Wiberg, Viktor; Servin, Martin; Nordfjell, Tomas: Discrete element modelling of large soil deformations under heavy vehicles. In: Journal of Terramechanics, vol. 93, pp. 11–21, 2021. @article{wiberg2021discrete,
title = {Discrete element modelling of large soil deformations under heavy vehicles},
author = {Viktor Wiberg and Martin Servin and Tomas Nordfjell},
url = {https://doi.org/10.1016/j.jterra.2020.10.002},
year = {2021},
date = {2021-01-01},
journal = {Journal of Terramechanics},
volume = {93},
pages = {11--21},
publisher = {Elsevier},
abstract = {This paper addresses the challenges of creating realistic models of soil for simulations of heavy vehicles on weak terrain. We modelled dense soils using the discrete element method with variable parameters for surface friction, normal cohesion, and rolling resistance. To find out what type of soils can be represented, we measured the internal friction and bulk cohesion of over 100 different virtual samples. To test the model, we simulated rut formation from a heavy vehicle with different loads and soil strengths. We conclude that the relevant space of dense frictional and frictional-cohesive soils can be represented and that the model is applicable for simulation of large deformations induced by heavy vehicles on weak terrain.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
This paper addresses the challenges of creating realistic models of soil for simulations of heavy vehicles on weak terrain. We modelled dense soils using the discrete element method with variable parameters for surface friction, normal cohesion, and rolling resistance. To find out what type of soils can be represented, we measured the internal friction and bulk cohesion of over 100 different virtual samples. To test the model, we simulated rut formation from a heavy vehicle with different loads and soil strengths. We conclude that the relevant space of dense frictional and frictional-cohesive soils can be represented and that the model is applicable for simulation of large deformations induced by heavy vehicles on weak terrain. |
 | Servin, Martin; Vesterlund, Folke; Wallin, Erik: Digital twins with embedded particle simulation. In: 14th World Congress in Computational Mechanics (WCCM) ECCOMAS Congress, Virtual, January 11-15, 2021, 2021. @inproceedings{servin2021digital,
title = {Digital twins with embedded particle simulation},
author = {Martin Servin and Folke Vesterlund and Erik Wallin},
url = {http://umu.diva-portal.org/smash/record.jsf?language=sv&pid=diva2%3A1499113&dswid=-3497
http://umu.diva-portal.org/smash/get/diva2:1499113/FULLTEXT01.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {14th World Congress in Computational Mechanics (WCCM) ECCOMAS Congress, Virtual, January 11-15, 2021},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {inproceedings}
}
|
 | Andersson, Jennifer; Bodin, Kenneth; Lindmark, Daniel; Servin, Martin; Wallin, Erik: Reinforcement Learning Control of a Forestry Crane Manipulator. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Sep. 27-Oct. 1st, 2021, Prague, Czech Republic (2021). arXiv:2103.02315, 2021. @article{andersson2021reinforcement,
title = {Reinforcement Learning Control of a Forestry Crane Manipulator},
author = {Jennifer Andersson and Kenneth Bodin and Daniel Lindmark and Martin Servin and Erik Wallin},
url = {https://arxiv.org/abs/2103.02315
https://arxiv.org/pdf/2103.02315
https://www.algoryx.se/papers/rlc-crane/
https://youtu.be/7xwMlS5uqxs},
year = {2021},
date = {2021-01-01},
journal = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Sep. 27-Oct. 1st, 2021, Prague, Czech Republic (2021). arXiv:2103.02315},
abstract = {Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation. |
 | Backman, Sofi; Lindmark, Daniel; Bodin, Kenneth; Servin, Martin; Mörk, Joakim; Löfgren, Håkan: Continuous control of an underground loader using deep reinforcement learning. In: Machines, vol. 9, no. 10, pp. 216, 2021. @article{backman2021continuous,
title = {Continuous control of an underground loader using deep reinforcement learning},
author = {Sofi Backman and Daniel Lindmark and Kenneth Bodin and Martin Servin and Joakim Mörk and Håkan Löfgren},
url = {https://www.mdpi.com/2075-1702/9/10/216
https://www.mdpi.com/2075-1702/9/10/216/pdf
https://www.algoryx.se/papers/drl-loader/
https://youtu.be/RzDTFZW26H0},
doi = { doi.org/10.3390/machines9100216},
year = {2021},
date = {2021-01-01},
journal = {Machines},
volume = {9},
number = {10},
pages = {216},
abstract = {Reinforcement learning control of an underground loader is investigated in simulated environment, using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of the pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point, while avoiding collisions, getting stuck, or losing ground traction. It relies on motion and force sensors, as well as on camera and lidar. Using a soft actor-critic algorithm the agents learn policies for efficient bucket filling over many subsequent loading cycles, with clear ability to adapt to the changing environment. The best results, on average 75% of the max capacity, are obtained when including a penalty for energy usage in the reward.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
Reinforcement learning control of an underground loader is investigated in simulated environment, using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of the pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point, while avoiding collisions, getting stuck, or losing ground traction. It relies on motion and force sensors, as well as on camera and lidar. Using a soft actor-critic algorithm the agents learn policies for efficient bucket filling over many subsequent loading cycles, with clear ability to adapt to the changing environment. The best results, on average 75% of the max capacity, are obtained when including a penalty for energy usage in the reward. |
 | Styrud, Jonathan; Iovino, Matteo; Norrlöf, Mikael; Björkman, Mårten; Smith, Christian: Combining Planning and Learning of Behavior Trees for Robotic Assembly. In: arXiv preprint arXiv:2103.09036, 2021. @article{styrud2021combining,
title = {Combining Planning and Learning of Behavior Trees for Robotic Assembly},
author = {Jonathan Styrud and Matteo Iovino and Mikael Norrlöf and Mårten Björkman and Christian Smith},
url = {https://arxiv.org/abs/2103.09036
https://arxiv.org/pdf/2103.09036
https://github.com/jstyrud/planning-and-learning
},
year = {2021},
date = {2021-01-01},
journal = {arXiv preprint arXiv:2103.09036},
abstract = {Industrial robots can solve very complex tasks in controlled environments, but modern applications require robots able to operate in unpredictable surroundings as well. An increasingly popular reactive policy architecture in robotics is Behavior Trees but as with other architectures, programming time still drives cost and limits flexibility. There are two main branches of algorithms to generate policies automatically, automated planning and machine learning, both with their own drawbacks. We propose a method for generating Behavior Trees using a Genetic Programming algorithm and combining the two branches by taking the result of an automated planner and inserting it into the population. Experimental results confirm that the proposed method of combining planning and learning performs well on a variety of robotic assembly problems and outperforms both of the base methods used separately. We also show that this type of high level learning of Behavior Trees can be transferred to a real system without further training.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
Industrial robots can solve very complex tasks in controlled environments, but modern applications require robots able to operate in unpredictable surroundings as well. An increasingly popular reactive policy architecture in robotics is Behavior Trees but as with other architectures, programming time still drives cost and limits flexibility. There are two main branches of algorithms to generate policies automatically, automated planning and machine learning, both with their own drawbacks. We propose a method for generating Behavior Trees using a Genetic Programming algorithm and combining the two branches by taking the result of an automated planner and inserting it into the population. Experimental results confirm that the proposed method of combining planning and learning performs well on a variety of robotic assembly problems and outperforms both of the base methods used separately. We also show that this type of high level learning of Behavior Trees can be transferred to a real system without further training. |
 | Gieselmann, Robert; Pokorny, Florian T: Planning-Augmented Hierarchical Reinforcement Learning. In: IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5097-5104, 2021. @article{Gieselmann2021,
title = {Planning-Augmented Hierarchical Reinforcement Learning},
author = {Robert Gieselmann and Florian T Pokorny},
doi = {10.1109/LRA.2021.3071062},
year = {2021},
date = {2021-01-01},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {3},
pages = {5097-5104},
abstract = {Abstract—Planning algorithms are powerful at solving longhorizon decision-making problems but require that environment dynamics are known. Model-free reinforcement learning has recently been merged with graph-based planning to increase the robustness of trained policies in state-space navigation problems. Recent ideas suggest to use planning in order to provide intermediate waypoints guiding the policy in long-horizon tasks. Yet, it is not always practical to describe a problem in the setting of state-to-state navigation. Often, the goal is defined by one or multiple disjoint sets of valid states or implicitly using an abstract task description. Building upon previous efforts, we introduce a novel algorithm called Planning-Augmented Hierarchical Reinforcement Learning (PAHRL) which translates the concept of hybrid planning/RL to such problems with implicitly defined goal. Using a hierarchical framework, we divide the original task, formulated as a Markov Decision Process (MDP), into a hierarchy of shorter horizon MDPs. Actor-critic agents are trained in parallel for each level of the hierarchy. During testing, a planner then determines useful subgoals on a state graph constructed at the bottom level of the hierarchy. The effectiveness of our approach is demonstrated for a set of continuous control problems in simulation including robot arm reaching tasks and the manipulation of a deformable object.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
Abstract—Planning algorithms are powerful at solving longhorizon decision-making problems but require that environment dynamics are known. Model-free reinforcement learning has recently been merged with graph-based planning to increase the robustness of trained policies in state-space navigation problems. Recent ideas suggest to use planning in order to provide intermediate waypoints guiding the policy in long-horizon tasks. Yet, it is not always practical to describe a problem in the setting of state-to-state navigation. Often, the goal is defined by one or multiple disjoint sets of valid states or implicitly using an abstract task description. Building upon previous efforts, we introduce a novel algorithm called Planning-Augmented Hierarchical Reinforcement Learning (PAHRL) which translates the concept of hybrid planning/RL to such problems with implicitly defined goal. Using a hierarchical framework, we divide the original task, formulated as a Markov Decision Process (MDP), into a hierarchy of shorter horizon MDPs. Actor-critic agents are trained in parallel for each level of the hierarchy. During testing, a planner then determines useful subgoals on a state graph constructed at the bottom level of the hierarchy. The effectiveness of our approach is demonstrated for a set of continuous control problems in simulation including robot arm reaching tasks and the manipulation of a deformable object. |
2020
|
 | Li, Guoyuan; Waldum, Håkon Bjerkgaard; Grindvik, Marcus Olai; Jørundl, Ruben Svedal; Zhang, Houxiang: Development of a vision-based target exploration system for snake-like robots in structured environments. In: International Journal of Advanced Robotic Systems, pp. 1-11, 2020. @article{Li2020,
title = {Development of a vision-based target exploration system for snake-like robots in structured environments},
author = {Guoyuan Li and Håkon Bjerkgaard Waldum and Marcus Olai Grindvik and Ruben Svedal Jørundl and Houxiang Zhang},
url = {https://journals.sagepub.com/doi/full/10.1177/1729881420936141
https://www.researchgate.net/publication/342710008_Development_of_a_vision-based_target_exploration_system_for_snake-like_robots_in_structured_environments},
doi = {10.1177/1729881420936141},
year = {2020},
date = {2020-07-06},
journal = {International Journal of Advanced Robotic Systems},
pages = {1-11},
abstract = {Applying snake-like robots to environmental exploration has been a hot topic for years. How to achieve free navigation for target search in a complex environment in a safe and efficient manner is one of the main tasks that researchers in the field of robotics currently face. This article presents a target exploration system that takes advantages of visual sensing to navigate the snake-like robot in structured environments. Two cameras are utilized in the system. The first one is mounted on the head of the snake-like robot for target recognition and the other is an overhead camera which is responsible for locating the robot and identifying surrounding obstacles. All dead ends in the environment can thus be recognized using a template-based method. A search strategy for traversal of the dead ends is employed for generating exploration paths. Several gaits are developed for the snake-like robot. By switching between these gaits, the snake-like robot is able to follow the paths to search for the target. Two experiments are conducted in a maze environment. The experimental results validate the effectiveness of the proposed system for snake-like robots exploring in structured environments.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
Applying snake-like robots to environmental exploration has been a hot topic for years. How to achieve free navigation for target search in a complex environment in a safe and efficient manner is one of the main tasks that researchers in the field of robotics currently face. This article presents a target exploration system that takes advantages of visual sensing to navigate the snake-like robot in structured environments. Two cameras are utilized in the system. The first one is mounted on the head of the snake-like robot for target recognition and the other is an overhead camera which is responsible for locating the robot and identifying surrounding obstacles. All dead ends in the environment can thus be recognized using a template-based method. A search strategy for traversal of the dead ends is employed for generating exploration paths. Several gaits are developed for the snake-like robot. By switching between these gaits, the snake-like robot is able to follow the paths to search for the target. Two experiments are conducted in a maze environment. The experimental results validate the effectiveness of the proposed system for snake-like robots exploring in structured environments. |
 | Andersson, Jennifer: Simulation-Driven Machine Learning Control of a Forestry Crane Manipulator. 2020. @mastersthesis{andersson2020simulation,
title = {Simulation-Driven Machine Learning Control of a Forestry Crane Manipulator},
author = {Jennifer Andersson},
url = {http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1507839&dswid=-3497
http://uu.diva-portal.org/smash/get/diva2:1507839/FULLTEXT01.pdf},
year = {2020},
date = {2020-01-01},
abstract = {A forwarder is a forestry vehicle carrying felled logs from the forest harvesting site, thereby constituting an essential part of the modern forest harvesting cycle. Successful automation efforts can increase productivity and improve operator working conditions, but despite increasing levels of automation in industry today, forwarders have remained manually operated. In our work, the grasping motion of a hydraulic-actuated forestry crane manipulator is automated in a simulated environment using state-of-the-art deep reinforcement learning methods. Two approaches for single-log grasping are investigated; amulti-agent approach and a single-agent approach based on curriculum learning. We show that both approaches can yield a high grasping success rate. Given the position and orientation of the target log, the best control policy is able to successfully grasp 97.4% of target logs.Including incentive for energy optimization, we are able to reduce theaverage energy consumption by 58.4% compared to the non-energy optimized model, while maintaining 82.9% of the success rate. The energy optimized control policy results in an overall smoother crane motion andacceleration profile during grasping. The results are promising and provide a natural starting point for end-to-end automation of forestry crane manipulators in the real world.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {mastersthesis}
}
A forwarder is a forestry vehicle carrying felled logs from the forest harvesting site, thereby constituting an essential part of the modern forest harvesting cycle. Successful automation efforts can increase productivity and improve operator working conditions, but despite increasing levels of automation in industry today, forwarders have remained manually operated. In our work, the grasping motion of a hydraulic-actuated forestry crane manipulator is automated in a simulated environment using state-of-the-art deep reinforcement learning methods. Two approaches for single-log grasping are investigated; amulti-agent approach and a single-agent approach based on curriculum learning. We show that both approaches can yield a high grasping success rate. Given the position and orientation of the target log, the best control policy is able to successfully grasp 97.4% of target logs.Including incentive for energy optimization, we are able to reduce theaverage energy consumption by 58.4% compared to the non-energy optimized model, while maintaining 82.9% of the success rate. The energy optimized control policy results in an overall smoother crane motion andacceleration profile during grasping. The results are promising and provide a natural starting point for end-to-end automation of forestry crane manipulators in the real world. |
 | Servin, Martin; Berglund, Tomas; Nystedt, Samuel: A multiscale model of terrain dynamics for real-time earthmoving simulation. In: arXiv preprint arXiv:2011.00459, 2020. @article{servin2020multiscale,
title = {A multiscale model of terrain dynamics for real-time earthmoving simulation},
author = {Martin Servin and Tomas Berglund and Samuel Nystedt},
url = {https://www.algoryx.se/papers/terrain/
https://arxiv.org/abs/2011.00459
https://arxiv.org/pdf/2011.00459.pdf
},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2011.00459},
abstract = {A multiscale model for real-time simulation of terrain dynamics is explored. To represent the dynamics on different scales the model combines the description of soil as a continuous solid, as distinct particles and as rigid multibodies. The models are dynamically coupled to each other and to the earthmoving equipment. Agitated soil is represented by a hybrid of contacting particles and continuum solid, with the moving equipment and resting soil as geometric boundaries. Each zone of active soil is aggregated into distinct bodies, with the proper mass, momentum and frictional-cohesive properties, which constrain the equipment's multibody dynamics. The particle model parameters are pre-calibrated to the bulk mechanical parameters for a wide range of different soils. The result is a computationally efficient model for earthmoving operations that resolve the motion of the soil, using a fast iterative solver, and provide realistic forces and dynamic for the equipment, using a direct solver for high numerical precision. Numerical simulations of excavation and bulldozing operations are performed to validate the model and measure the computational performance. Reference data is produced using coupled discrete element and multibody dynamics simulations at relatively high resolution. The digging resistance and soil displacements with the real-time multiscale model agree with the reference model up to 10-25%, and run more than three orders of magnitude faster.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
A multiscale model for real-time simulation of terrain dynamics is explored. To represent the dynamics on different scales the model combines the description of soil as a continuous solid, as distinct particles and as rigid multibodies. The models are dynamically coupled to each other and to the earthmoving equipment. Agitated soil is represented by a hybrid of contacting particles and continuum solid, with the moving equipment and resting soil as geometric boundaries. Each zone of active soil is aggregated into distinct bodies, with the proper mass, momentum and frictional-cohesive properties, which constrain the equipment's multibody dynamics. The particle model parameters are pre-calibrated to the bulk mechanical parameters for a wide range of different soils. The result is a computationally efficient model for earthmoving operations that resolve the motion of the soil, using a fast iterative solver, and provide realistic forces and dynamic for the equipment, using a direct solver for high numerical precision. Numerical simulations of excavation and bulldozing operations are performed to validate the model and measure the computational performance. Reference data is produced using coupled discrete element and multibody dynamics simulations at relatively high resolution. The digging resistance and soil displacements with the real-time multiscale model agree with the reference model up to 10-25%, and run more than three orders of magnitude faster. |
 | Laezza, Rita; Karayiannidis, Yiannis: Shape Control of Elastoplastic Deformable Linear Objects through Reinforcement Learning. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Las Vegas (Virtual), USA, 2020-10-25 - 2020-10-29, 2020. @conference{laezzashape2020,
title = {Shape Control of Elastoplastic Deformable Linear Objects through Reinforcement Learning},
author = {Rita Laezza and Yiannis Karayiannidis},
url = {https://ras.papercept.net/proceedings/IROS20/3496.pdf},
year = {2020},
date = {2020-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Las Vegas (Virtual), USA, 2020-10-25 - 2020-10-29},
abstract = {Deformable object manipulation tasks have longbeen regarded as challenging robotic problems. However, untilrecently, very little work had been done on the subject, withmost robotic manipulation methods being developed for rigidobjects. As machine learning methods are becoming morepowerful, there are new model-free strategies to explore forthese objects, which are notoriously hard to model. This paperfocuses on shape control problems for Deformable Linear Objects (DLOs). Despite being one of the most researched classesof DLOs in terms of geometry, no other paper has focusedon materials with elastoplastic properties. Therefore, a novelshape control task, requiring permanent plastic deformationis implemented in a simulation environment. ReinforcementLearning methods are used to learn a continuous controlpolicy. To that end, a discrete curvature measure is usedas a low-dimensional state representation and as part of anintuitive reward function. Finally, three state-of-the-art actor-critic algorithms are compared on the proposed environmentand successfully achieve the goal shape.},
keywords = {External},
pubstate = {published},
tppubtype = {conference}
}
Deformable object manipulation tasks have longbeen regarded as challenging robotic problems. However, untilrecently, very little work had been done on the subject, withmost robotic manipulation methods being developed for rigidobjects. As machine learning methods are becoming morepowerful, there are new model-free strategies to explore forthese objects, which are notoriously hard to model. This paperfocuses on shape control problems for Deformable Linear Objects (DLOs). Despite being one of the most researched classesof DLOs in terms of geometry, no other paper has focusedon materials with elastoplastic properties. Therefore, a novelshape control task, requiring permanent plastic deformationis implemented in a simulation environment. ReinforcementLearning methods are used to learn a continuous controlpolicy. To that end, a discrete curvature measure is usedas a low-dimensional state representation and as part of anintuitive reward function. Finally, three state-of-the-art actor-critic algorithms are compared on the proposed environmentand successfully achieve the goal shape. |
 | Major, Pierre; Zhang, Houxiang; Hildre, Hans Petter; Edet, Mathieu: Virtual prototyping of offshore operations: a review. In: Ship Technology Research, pp. 1–18, 2020. @article{major2020virtual,
title = {Virtual prototyping of offshore operations: a review},
author = {Pierre Major and Houxiang Zhang and Hans Petter Hildre and Mathieu Edet},
doi = {10.1080/09377255.2020.1831840},
year = {2020},
date = {2020-01-01},
journal = {Ship Technology Research},
pages = {1--18},
publisher = {Taylor & Francis},
abstract = {Virtual prototyping of offshore operations (VPOO) is performed to plan and validate planning of infrequent or demanding operations characterized by high risk and low margins of error in hostile and remote environments distant from emergency response bases that require expensive equipment. Key elements of VPOO is the rapidity of virtual prototyping and the human-centric approach necessitating high quality visuals and real-time time-domain simulation. This survey reviews publications, commercial software and simulators, and regulations on offshore operations. Findings indicate that the VPOO is not common in the industry, offshore operation regulations lag behind the state of the art in industry in terms of mission planning, and this field has been subject to scarce commercial and scientific scrutiny so far. A discussion of future developments and trends concludes the paper.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
Virtual prototyping of offshore operations (VPOO) is performed to plan and validate planning of infrequent or demanding operations characterized by high risk and low margins of error in hostile and remote environments distant from emergency response bases that require expensive equipment. Key elements of VPOO is the rapidity of virtual prototyping and the human-centric approach necessitating high quality visuals and real-time time-domain simulation. This survey reviews publications, commercial software and simulators, and regulations on offshore operations. Findings indicate that the VPOO is not common in the industry, offshore operation regulations lag behind the state of the art in industry in terms of mission planning, and this field has been subject to scarce commercial and scientific scrutiny so far. A discussion of future developments and trends concludes the paper. |
 | Yang, Yajue; Pan, Jia; Long, Pinxin; Song, Xibin; Zhang, Liangjun: Time Variable Minimum Torque Trajectory Optimization for Autonomous Excavator. In: arXiv preprint arXiv:2006.00811, 2020. @article{yang2020time,
title = {Time Variable Minimum Torque Trajectory Optimization for Autonomous Excavator},
author = {Yajue Yang and Jia Pan and Pinxin Long and Xibin Song and Liangjun Zhang},
url = {https://arxiv.org/abs/2006.00811
https://arxiv.org/pdf/2006.00811},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2006.00811},
abstract = {In this paper, we present a minimal torque and time variable trajectory optimization method for autonomous excavator considering the soil-tool interaction. The method formulates the excavation motion generation as a trajectory optimization problem and takes into account geometric, kinematic and dynamics constraints. To generate time-efficient trajectory and improve the overall optimization efficiency, we propose a time variable trajectory optimization mechanism so that the time intervals between the keypoints along the trajectory subject to the optimization. As a result, the method uses few keypoints and reduces the total number of optimization variables. We further introduce a soil-tool interaction force model, which considers the geometric shape of the bucket and the physical properties of the soil. The experimental result on a high fidelity dynamic simulator shows our method can generate feasible trajectories, which satisfy excavation task constraints and are adaptive to different soil conditions.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a minimal torque and time variable trajectory optimization method for autonomous excavator considering the soil-tool interaction. The method formulates the excavation motion generation as a trajectory optimization problem and takes into account geometric, kinematic and dynamics constraints. To generate time-efficient trajectory and improve the overall optimization efficiency, we propose a time variable trajectory optimization mechanism so that the time intervals between the keypoints along the trajectory subject to the optimization. As a result, the method uses few keypoints and reduces the total number of optimization variables. We further introduce a soil-tool interaction force model, which considers the geometric shape of the bucket and the physical properties of the soil. The experimental result on a high fidelity dynamic simulator shows our method can generate feasible trajectories, which satisfy excavation task constraints and are adaptive to different soil conditions. |
 | Maruyama, T; Ogawa, S; Noda, K; Edaya, M; Jaklin, N; Tolsma, S; Takeda, N: Structural displacement compensation of a gigantic manipulator via deep learning. In: 2020 IEEE/SICE International Symposium on System Integration (SII), pp. 219–224, IEEE 2020. @inproceedings{maruyama2020structural,
title = {Structural displacement compensation of a gigantic manipulator via deep learning},
author = {T Maruyama and S Ogawa and K Noda and M Edaya and N Jaklin and S Tolsma and N Takeda},
doi = {10.1109/SII46433.2020.9026263},
year = {2020},
date = {2020-01-01},
booktitle = {2020 IEEE/SICE International Symposium on System Integration (SII)},
pages = {219--224},
organization = {IEEE},
abstract = {Structures of robotic systems that handle extremely heavy loads undergo static displacement. The ITER blanket remote handling system, which handles 4-ton objects, has displacements of up to 100 mm at the end effector. We propose a novel method that combines deep learning with a physics-based virtual reality system to compensate for displacement. Our deep learning model was trained by using data obtained from both the virtual reality system and physical measurement data of end effector positions. By using a prototype of the ITER blanket remote handling system, we experimentally show that our method successfully reduces the displacement at the end effector to a maximum error of 5.7 mm and a median error of 1.2 mm. We conclude that our approach provides an effective contribution to ensuring the feasibility and safety of the remote maintenance procedures that are to be performed within the ITER project.},
keywords = {External},
pubstate = {published},
tppubtype = {inproceedings}
}
Structures of robotic systems that handle extremely heavy loads undergo static displacement. The ITER blanket remote handling system, which handles 4-ton objects, has displacements of up to 100 mm at the end effector. We propose a novel method that combines deep learning with a physics-based virtual reality system to compensate for displacement. Our deep learning model was trained by using data obtained from both the virtual reality system and physical measurement data of end effector positions. By using a prototype of the ITER blanket remote handling system, we experimentally show that our method successfully reduces the displacement at the end effector to a maximum error of 5.7 mm and a median error of 1.2 mm. We conclude that our approach provides an effective contribution to ensuring the feasibility and safety of the remote maintenance procedures that are to be performed within the ITER project. |
 | Yuan, Shuai; Major, Pierre; Zhang, Houxiang: Flexible riser replacement operation based on advanced virtual prototyping. In: Ocean Engineering, vol. 210, pp. 107502, 2020. @article{yuan2020flexible,
title = {Flexible riser replacement operation based on advanced virtual prototyping},
author = {Shuai Yuan and Pierre Major and Houxiang Zhang},
doi = {10.1016/j.oceaneng.2020.107502},
year = {2020},
date = {2020-01-01},
journal = {Ocean Engineering},
volume = {210},
pages = {107502},
publisher = {Elsevier},
abstract = {As a critical campaign in the offshore oil and gas engineering, flexible riser replacements involve complex operations that need to be optimized and detailed to factor in trends in the industry. Since it allows engineers to interact with simulation tools in real time during the operation design phase, virtual prototyping (VP) is an efficient method to obtain an optimal solution and improve operational procedures in terms of safety and effectiveness for risk-based integrity management of flexible risers. In this study, a real-time VP model is adopted to simulate the process of a water injection flexible riser pulled in from an installation vessel to a jacket platform, which is one of the riser replacements tasks. The results are validated against results based on a finite element analysis. Attention is paid to the configuration, tension, and maximum bending curvature along the flexible riser during the operation. The innovative approach presented in this paper can provide guidance with respect to the operation limitations of a flexible pipe in practical engineering.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
As a critical campaign in the offshore oil and gas engineering, flexible riser replacements involve complex operations that need to be optimized and detailed to factor in trends in the industry. Since it allows engineers to interact with simulation tools in real time during the operation design phase, virtual prototyping (VP) is an efficient method to obtain an optimal solution and improve operational procedures in terms of safety and effectiveness for risk-based integrity management of flexible risers. In this study, a real-time VP model is adopted to simulate the process of a water injection flexible riser pulled in from an installation vessel to a jacket platform, which is one of the riser replacements tasks. The results are validated against results based on a finite element analysis. Attention is paid to the configuration, tension, and maximum bending curvature along the flexible riser during the operation. The innovative approach presented in this paper can provide guidance with respect to the operation limitations of a flexible pipe in practical engineering. |
 | Suzuki, Kenta; Kawabata, Kuniaki: Development of a simulator for underwater reconnaissance tasks by utilizing remotely operated robots. In: 2020 IEEE/SICE International Symposium on System Integration (SII), pp. 1100–1106, IEEE 2020. @inproceedings{suzuki2020development,
title = {Development of a simulator for underwater reconnaissance tasks by utilizing remotely operated robots},
author = {Kenta Suzuki and Kuniaki Kawabata},
doi = {10.1109/SII46433.2020.9026281},
year = {2020},
date = {2020-01-01},
booktitle = {2020 IEEE/SICE International Symposium on System Integration (SII)},
pages = {1100--1106},
organization = {IEEE},
abstract = {This paper describes the development of a simulator for underwater reconnaissance tasks by utilizing remotely operated robots. The developed simulator replicates physical effect such as fluid dynamics, buoyancy and fluid resistance in the area assumed to be filled with water. The simulated thrusters generate propulsion force and torque. The simulator also provides camera view disturbance models for a blur, distortion and noise. In this paper, we discussed the requirements to realistically simulate underwater remote reconnaissance tasks and explain the implementation methodologies. By using the developed simulator, we also demonstrate a simulation of a remotely operated vehicle (ROV) that was utilized in Fukushima Daiichi Nuclear Power Station (FDNPS).},
keywords = {External},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes the development of a simulator for underwater reconnaissance tasks by utilizing remotely operated robots. The developed simulator replicates physical effect such as fluid dynamics, buoyancy and fluid resistance in the area assumed to be filled with water. The simulated thrusters generate propulsion force and torque. The simulator also provides camera view disturbance models for a blur, distortion and noise. In this paper, we discussed the requirements to realistically simulate underwater remote reconnaissance tasks and explain the implementation methodologies. By using the developed simulator, we also demonstrate a simulation of a remotely operated vehicle (ROV) that was utilized in Fukushima Daiichi Nuclear Power Station (FDNPS). |
 | Borgström, Johan: Methodology for Real Time Simulations of Autonomous Utility Vehicles. 2020. @misc{borgstrom2020methodology,
title = {Methodology for Real Time Simulations of Autonomous Utility Vehicles},
author = {Johan Borgström},
url = {http://ltu.diva-portal.org/smash/record.jsf?pid=diva2%3A1437952&dswid=-7032
http://ltu.diva-portal.org/smash/get/diva2:1437952/FULLTEXT02.pdf},
year = {2020},
date = {2020-01-01},
abstract = {This master thesis is a part of a research project where Luleå University of Technology (LTU) collaborates with University of Oulu, SINTEF Narvik and Oulu University of Applied Sciences. The goal with the research project is to develop a Nordic platform for development of autonomous, environmental friendly and energy efficient heavy vehicles in the forest, harbor and mining industry. The purpose with the master thesis is to assist LTU in their role in the research project. The Nordic platform was positioned in the product development process, with the result that it could be useful in the fourth phase ”Detail design” and in the fifth phase ”Testing and refinement” in the Ulrich and Eppinger product development process. A methodology has been developed, covering all necessary steps going from an assembly of a vehicle in an arbitrary CAD program to perform real time simulations (including HiL simulations) of the vehicle in Simulink. The off-road research platform for forest- and agriculture applications developed by LTU was used as a case study in the master thesis. Applying the methodology on this platform showed that choosing correct simulation frequency is important and that graphics enabled in real time simulations requires large computational power.},
keywords = {External},
pubstate = {published},
tppubtype = {misc}
}
This master thesis is a part of a research project where Luleå University of Technology (LTU) collaborates with University of Oulu, SINTEF Narvik and Oulu University of Applied Sciences. The goal with the research project is to develop a Nordic platform for development of autonomous, environmental friendly and energy efficient heavy vehicles in the forest, harbor and mining industry. The purpose with the master thesis is to assist LTU in their role in the research project. The Nordic platform was positioned in the product development process, with the result that it could be useful in the fourth phase ”Detail design” and in the fifth phase ”Testing and refinement” in the Ulrich and Eppinger product development process. A methodology has been developed, covering all necessary steps going from an assembly of a vehicle in an arbitrary CAD program to perform real time simulations (including HiL simulations) of the vehicle in Simulink. The off-road research platform for forest- and agriculture applications developed by LTU was used as a case study in the master thesis. Applying the methodology on this platform showed that choosing correct simulation frequency is important and that graphics enabled in real time simulations requires large computational power. |
 | Pereira, JG; Ellman, A: From CAD to Physics-Based Digital Twin: Framework for Real-Time Simulation of Virtual prototypes. In: Proceedings of the Design Society: DESIGN Conference, pp. 335–344, Cambridge University Press 2020. @inproceedings{pereira2020cad,
title = {From CAD to Physics-Based Digital Twin: Framework for Real-Time Simulation of Virtual prototypes},
author = {JG Pereira and A Ellman},
url = {https://www.cambridge.org/core/services/aop-cambridge-core/content/view/479604733E3644CA38B927FFCC09B519/S2633776220000473a.pdf/from-cad-to-physics-based-digital-twin-framework-for-real-time-simulation-of-virtual-prototypes.pdf},
doi = {doi:10.1017/dsd.2020.47},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the Design Society: DESIGN Conference},
volume = {1},
pages = {335--344},
organization = {Cambridge University Press},
abstract = {Engineering work is mostly done in 3D CAD software throughout the engineering process from conceptual design and layout of products. Physics-Based Virtual Prototypes are very valuable addition on Computer Aided Engineering enabling product development simulators, training simulators and digital twin concept in product lift-cycle process. In this work, we present a framework, how such virtual prototypes can be developed from 3D CAD models with meaningful effort.},
keywords = {External},
pubstate = {published},
tppubtype = {inproceedings}
}
Engineering work is mostly done in 3D CAD software throughout the engineering process from conceptual design and layout of products. Physics-Based Virtual Prototypes are very valuable addition on Computer Aided Engineering enabling product development simulators, training simulators and digital twin concept in product lift-cycle process. In this work, we present a framework, how such virtual prototypes can be developed from 3D CAD models with meaningful effort. |
 | Vikdahl, Martin: Streaming Data Models for Distributed Physics Simulation Workflows. Department of Computing Science, Umeå University, Sweden, 2020. @mastersthesis{Vikdahl2020,
title = {Streaming Data Models for Distributed Physics Simulation Workflows},
author = {Martin Vikdahl},
url = {http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Aumu%3Adiva-177599
https://www.algoryx.se/mainpage/wp-content/uploads/2021/04/Thesis-Martin.Vikdahl-Streaming.data_.models.for-distributed.physics.simulation.workflows.pdf},
year = {2020},
date = {2020-01-01},
school = {Department of Computing Science, Umeå University, Sweden},
abstract = {This project explores the possibility of lowering the barrier of entry for integrating a physics engine into distributed organization-specific pipelines by providing an interface for communicating over the network between domain-specific tools. The approach uses an event-driven interface, both for transferring simulation models incrementally as event streams and for suggesting modifications of the models.
The proposed architecture uses a technique for storing and managing different versions of the simulation models that roughly aligns with the concept of event sourcing and allowed for communicating updates to models by only sending information about what had changed since the older version. The architecture also has a simple dependency management system between models that takes versioning into account by causally ordering dependencies. The solution allows for multiple simultaneous client users which could support connecting collaborative editing and visualization tools.
By implementing a prototype of the architecture it was concluded that the format could encode models into a compact stream of small, autonomous event messages, that could be used to replicate the original structure on the receiving end, but it was difficult to make a good quantitative evaluation without access to a large collection of representative example models, because the size distributions depended on the usage.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {mastersthesis}
}
This project explores the possibility of lowering the barrier of entry for integrating a physics engine into distributed organization-specific pipelines by providing an interface for communicating over the network between domain-specific tools. The approach uses an event-driven interface, both for transferring simulation models incrementally as event streams and for suggesting modifications of the models.
The proposed architecture uses a technique for storing and managing different versions of the simulation models that roughly aligns with the concept of event sourcing and allowed for communicating updates to models by only sending information about what had changed since the older version. The architecture also has a simple dependency management system between models that takes versioning into account by causally ordering dependencies. The solution allows for multiple simultaneous client users which could support connecting collaborative editing and visualization tools.
By implementing a prototype of the architecture it was concluded that the format could encode models into a compact stream of small, autonomous event messages, that could be used to replicate the original structure on the receiving end, but it was difficult to make a good quantitative evaluation without access to a large collection of representative example models, because the size distributions depended on the usage. |
 | Asplund, Philip: Real-Time Spherical Discretization. Department of Computing Science, Umeå University, Sweden, 2020. @mastersthesis{Asplund2020,
title = {Real-Time Spherical Discretization},
author = {Philip Asplund},
url = {https://www.algoryx.se/mainpage/wp-content/uploads/2021/04/REAL-TIME-SPHERICAL-DISCRETIZATION-Surface-rendering-and-upscaling-Philip-Asplund-Master_Thesis.pdf},
year = {2020},
date = {2020-01-01},
school = {Department of Computing Science, Umeå University, Sweden},
abstract = {This thesis explores a method for upscaling and increasing the visual fidelity of coarse soil simulation. This is done through the use of a High Resolution (HR)- based method that guides fine-scale particles which are then rendered using either surface rendering or rendering with particle meshes. This thesis also explores the idea of omitting direct calculation of the internal and external forces, and instead only use the velocity voxel grid generated from the coarse simulation. This is done to determine if the method can still reproduce natural soil movements of the fine-scale particles when simulating and rendering under realtime constraints.
The result shows that this method increases the visual fidelity of the rendering without a significant impact on the overall simulation run-time performance, while the fine-scale particles still produce movements that are perceived as natural. It also shows that the use of surface rendering does not need as high fine-scale particle resolution for the same perceived visual soil fidelity as when rendering with particle mesh.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {mastersthesis}
}
This thesis explores a method for upscaling and increasing the visual fidelity of coarse soil simulation. This is done through the use of a High Resolution (HR)- based method that guides fine-scale particles which are then rendered using either surface rendering or rendering with particle meshes. This thesis also explores the idea of omitting direct calculation of the internal and external forces, and instead only use the velocity voxel grid generated from the coarse simulation. This is done to determine if the method can still reproduce natural soil movements of the fine-scale particles when simulating and rendering under realtime constraints.
The result shows that this method increases the visual fidelity of the rendering without a significant impact on the overall simulation run-time performance, while the fine-scale particles still produce movements that are perceived as natural. It also shows that the use of surface rendering does not need as high fine-scale particle resolution for the same perceived visual soil fidelity as when rendering with particle mesh. |
2019
|
 | Servin, Martin; Wallin, Erik: Reduced order modeling for realtime simulation with granular materials. In: VI International Conference on Particle-Based Methods-Fundamentals and Applications-PARTICLES. Barcelona, Spain, October 28-30, 2019, 2019. @inproceedings{servin2019reduced,
title = {Reduced order modeling for realtime simulation with granular materials},
author = {Martin Servin and Erik Wallin},
url = {http://umu.diva-portal.org/smash/record.jsf?language=sv&pid=diva2%3A1319374&dswid=-61
http://umu.diva-portal.org/smash/get/diva2:1319374/SUMMARY01.pdf
},
year = {2019},
date = {2019-01-01},
booktitle = {VI International Conference on Particle-Based Methods-Fundamentals and Applications-PARTICLES. Barcelona, Spain, October 28-30, 2019},
abstract = {The discrete element method (DEM) is a versatile but computationally intense method for simulation of granular materials. It is therefore rarely used in applications that require realtime performance, e.g, interactive simulaions with a human operator or hardware in the loop.
We investigate the use of reduced order modeling for achieving realtime performance in coupled discrete element and rigid multibody simulations. First, a large data set is produced from a series of simulations that cover a selected state-space. The particle data is coarse-grained into discrete field variables, representing mass density, velocity, strain and stress. A reduced order representation of the state-space is identified. Different methods for predicting the fields are explored, given certain observations and assumptions about the state of the simulation e.g., motion of boundaries, rigid bodies or control signals. The particle positions and velocities can then be advanced in time using the predicted fields plus a model for particle diffusion [4] and a local incompressibility constraint [1]. The resulting method can be seen as an extension to the one in [5], by extending the reduced space from rigid body motion of particle aggregates to a low-dimensional space of flow fields [2, 3].
The precision and computational performance of the reduced order simulation method is analyzed on simple test systems, including silo flow and a blade cutting a granular bed. Finally, coupled simulation of an articulated rigid multibody system and a reduced order granular system is demonstrated.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {inproceedings}
}
The discrete element method (DEM) is a versatile but computationally intense method for simulation of granular materials. It is therefore rarely used in applications that require realtime performance, e.g, interactive simulaions with a human operator or hardware in the loop.
We investigate the use of reduced order modeling for achieving realtime performance in coupled discrete element and rigid multibody simulations. First, a large data set is produced from a series of simulations that cover a selected state-space. The particle data is coarse-grained into discrete field variables, representing mass density, velocity, strain and stress. A reduced order representation of the state-space is identified. Different methods for predicting the fields are explored, given certain observations and assumptions about the state of the simulation e.g., motion of boundaries, rigid bodies or control signals. The particle positions and velocities can then be advanced in time using the predicted fields plus a model for particle diffusion [4] and a local incompressibility constraint [1]. The resulting method can be seen as an extension to the one in [5], by extending the reduced space from rigid body motion of particle aggregates to a low-dimensional space of flow fields [2, 3].
The precision and computational performance of the reduced order simulation method is analyzed on simple test systems, including silo flow and a blade cutting a granular bed. Finally, coupled simulation of an articulated rigid multibody system and a reduced order granular system is demonstrated. |
 | Major, Pierre; Skulstad, Robert; Li, Guoyuan; Zhang, Houxiang: Virtual prototyping: a case study of positioning systems for drilling operations in the Barents Sea. In: Ships and Offshore Structures, vol. 14, no. sup1, pp. 364–373, 2019. @article{major2019virtual,
title = {Virtual prototyping: a case study of positioning systems for drilling operations in the Barents Sea},
author = {Pierre Major and Robert Skulstad and Guoyuan Li and Houxiang Zhang},
doi = {10.1080/17445302.2019.1601322},
year = {2019},
date = {2019-01-01},
journal = {Ships and Offshore Structures},
volume = {14},
number = {sup1},
pages = {364--373},
publisher = {Taylor & Francis},
abstract = {This study proposes a framework for comparative study on three different positioning solutions for mobile offshore drilling units (MODUs) using high modulus polyethylene (HMPE) ropes, including active mooring with an HMPE rope, conventional dynamic positioning (DP) and active hybrid position-keeping (AHP-K). The goal of the positioning systems is to keep the MODU above the wellhead with acceptable riser-angle loading, minimal energy consumption, reduced underwater noise generation, and harmful emissions. This is the first time a holistic study has been performed on positioning that factors in the financial and environmental costs. The time domain simulation, which includes sea-state, wind, and current profiles, is performed with a well-developed software architecture and control algorithms for MODU position-keeping. The case study addresses a MODU drilling in the Barents Sea. Simulation results show that AHP-K is more efficient compared to the other two positioning solutions for drilling operations in the studied environment.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
This study proposes a framework for comparative study on three different positioning solutions for mobile offshore drilling units (MODUs) using high modulus polyethylene (HMPE) ropes, including active mooring with an HMPE rope, conventional dynamic positioning (DP) and active hybrid position-keeping (AHP-K). The goal of the positioning systems is to keep the MODU above the wellhead with acceptable riser-angle loading, minimal energy consumption, reduced underwater noise generation, and harmful emissions. This is the first time a holistic study has been performed on positioning that factors in the financial and environmental costs. The time domain simulation, which includes sea-state, wind, and current profiles, is performed with a well-developed software architecture and control algorithms for MODU position-keeping. The case study addresses a MODU drilling in the Barents Sea. Simulation results show that AHP-K is more efficient compared to the other two positioning solutions for drilling operations in the studied environment. |
 | Suzuki, Kenta; Kawabata, Kuniaki: Development of a multi-copter simulator and a projection system for virtual operation experience. In: 2019 IEEE/SICE International Symposium on System Integration (SII), pp. 1–6, IEEE 2019. @inproceedings{suzuki2019development,
title = {Development of a multi-copter simulator and a projection system for virtual operation experience},
author = {Kenta Suzuki and Kuniaki Kawabata},
doi = {10.1109/SII.2019.8700412},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE/SICE International Symposium on System Integration (SII)},
pages = {1--6},
organization = {IEEE},
abstract = {Our motivation is to utilize simulation technology to accelerate the decommissioning of Fukushima Daiichi Nuclear Power Station (FDNPS) by remote operated robots. We already developed several simulation functions in our previous work. Recently multi-copter was utilized for reconnaissance tasks at FDNPS. This paper described to design a simulation function for multi-copter operation training. Fluid dynamics affected to flying body are simulated by implemented function. We also attempt the demonstration of immersive operation environment based on a 3D projection system that provides virtual operation experience.},
keywords = {External},
pubstate = {published},
tppubtype = {inproceedings}
}
Our motivation is to utilize simulation technology to accelerate the decommissioning of Fukushima Daiichi Nuclear Power Station (FDNPS) by remote operated robots. We already developed several simulation functions in our previous work. Recently multi-copter was utilized for reconnaissance tasks at FDNPS. This paper described to design a simulation function for multi-copter operation training. Fluid dynamics affected to flying body are simulated by implemented function. We also attempt the demonstration of immersive operation environment based on a 3D projection system that provides virtual operation experience. |
 | Wang, Lin; Kim, Hyuncheol; Kim, Imgyu; Han, Soonhung: A visual simulation of ocean floating wind power system. In: Computer Animation and Virtual Worlds, vol. 30, no. 2, pp. e1859, 2019. @article{wang2019visual,
title = {A visual simulation of ocean floating wind power system},
author = {Lin Wang and Hyuncheol Kim and Imgyu Kim and Soonhung Han},
doi = {10.1002/cav.1859},
year = {2019},
date = {2019-01-01},
journal = {Computer Animation and Virtual Worlds},
volume = {30},
number = {2},
pages = {e1859},
publisher = {Wiley Online Library},
abstract = {The development of ocean floating wind power has been burgeoning in recent years because of its low cost and high efficiency. To facilitate effectiveness, 3D visualization using virtual reality and augmented reality technologies has been applied to many operating systems. However, most of the existing 3D motion visualizations are “pseudo” visualization, and there are a few realistic visualization systems that base the motion of ocean floating wind power on simulation and experiment results. Therefore, in this paper, we conducted research related to the design for a realistic motion visualization system based on numerical simulation data using a commercial game engine (Unity 3D). In our system, the six‐degree‐of‐freedom motion (Surge, Sway, Heave, Roll, Pitch, and Yaw) is simulated and visualized based on numerical analysis results of two hydrodynamics simulation softwares, which can illuminate the nuance between simulation results and experiment results and give us a “real‐time” visual experience about motion in each direction. Meanwhile, comprehensive sea environment conditions, such as wind, rain, water, sound, and cloudiness, are also visualized in Unity 3D.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}
The development of ocean floating wind power has been burgeoning in recent years because of its low cost and high efficiency. To facilitate effectiveness, 3D visualization using virtual reality and augmented reality technologies has been applied to many operating systems. However, most of the existing 3D motion visualizations are “pseudo” visualization, and there are a few realistic visualization systems that base the motion of ocean floating wind power on simulation and experiment results. Therefore, in this paper, we conducted research related to the design for a realistic motion visualization system based on numerical simulation data using a commercial game engine (Unity 3D). In our system, the six‐degree‐of‐freedom motion (Surge, Sway, Heave, Roll, Pitch, and Yaw) is simulated and visualized based on numerical analysis results of two hydrodynamics simulation softwares, which can illuminate the nuance between simulation results and experiment results and give us a “real‐time” visual experience about motion in each direction. Meanwhile, comprehensive sea environment conditions, such as wind, rain, water, sound, and cloudiness, are also visualized in Unity 3D. |
 | Kanehiro, Fumio; Nakaoka, Shin’ichiro; Sugihara, Tomomichi; Wakisaka, Naoki; Ishigami, Genya; Ozaki, Shingo; Tadokoro, Satoshi: Simulator for disaster response robotics. In: S., Tadokoro (Ed.): Disaster Robotics, vol. 128, pp. 453–477, Springer, 2019. @incollection{kanehiro2019simulator,
title = {Simulator for disaster response robotics},
author = {Fumio Kanehiro and Shin’ichiro Nakaoka and Tomomichi Sugihara and Naoki Wakisaka and Genya Ishigami and Shingo Ozaki and Satoshi Tadokoro},
editor = {Tadokoro S. },
doi = {10.1007/978-3-030-05321-5_9},
year = {2019},
date = {2019-01-01},
booktitle = {Disaster Robotics},
volume = {128},
pages = {453--477},
publisher = {Springer},
series = {Springer Tracts in Advanced Robotics},
abstract = {This chapter presents a simulator for disaster response robots based on the Choreonoid framework. Two physics engines and a graphics engine were developed and integrated into the framework. One physics engine enables robust contact-force computation among rigid bodies based on volumetric intersection and a relaxed constraint, whereas the other enables accurate and computationally efficient computation of machine–terrain interaction mechanics based on macro and microscopic approaches. The graphics engine allows simulating natural phenomena, such as rain, fire, and smoke, based on a particle system to resemble tough scenarios at disaster sites. In addition, wide-angle vision sensors, such as omnidirectional cameras and LIDAR sensors, can be simulated using multiple rendering screens. Overall, the simulator provides a tool for the efficient and safe development of disaster response robots.},
keywords = {External},
pubstate = {published},
tppubtype = {incollection}
}
This chapter presents a simulator for disaster response robots based on the Choreonoid framework. Two physics engines and a graphics engine were developed and integrated into the framework. One physics engine enables robust contact-force computation among rigid bodies based on volumetric intersection and a relaxed constraint, whereas the other enables accurate and computationally efficient computation of machine–terrain interaction mechanics based on macro and microscopic approaches. The graphics engine allows simulating natural phenomena, such as rain, fire, and smoke, based on a particle system to resemble tough scenarios at disaster sites. In addition, wide-angle vision sensors, such as omnidirectional cameras and LIDAR sensors, can be simulated using multiple rendering screens. Overall, the simulator provides a tool for the efficient and safe development of disaster response robots. |
 | João, Pereira Jr: Development of a Harvester Machine Simulator in Virtual Reality. Faculty of Engineering and Natural Sciences, Tampere University, 2019. @mastersthesis{pereira2019development,
title = {Development of a Harvester Machine Simulator in Virtual Reality},
author = {Pereira Jr João},
url = {https://trepo.tuni.fi/bitstream/handle/10024/115646/Pereira.pdf?sequence=2},
year = {2019},
date = {2019-01-01},
school = {Faculty of Engineering and Natural Sciences, Tampere University},
abstract = {Computer-aided design (CAD) software is used in the product design and development to design complex and detailed prototypes. It provides good assistance and solid data generation to designers and engineers. In order to remain competitive, industry is always seeking for higher process efficiency and product quality enhancement in the shortest period of time. Continuous research keeps going to make it possible.
Virtual reality has been one of the research focus in the recent years. It is studied and applied to be used as an assistant tool in the product lifecyle management, particularly in facilitating the development phase. However, the implementation process from CAD to virtual reality remains a challenge due to time consumption and technology complexibility.
In this work a real-time virtual reality harvester simulator was developed. The start point was a 3D harvester CAD model. It was used the CAD simulator AGX Momentum, a game engine Unity
and the physics engine AGX Dynamics to create dynamics simulation, to design a virtual forest environment and to enable physical controllers interact with the model.
With the capabilities of AGX Momentum, it was added dynamics motion directly in the CAD software, creating fast CAD simulations. A virtual scene was designed with Unity to simulate an environment and the immersion of the user on it with Oculus Rift device. The harvester model was imported to the Unity scene with AGX Dynamics.
In the end it was obtained a real size virtual prototype, with the possibility of interacting and control it using physical controllers. The user can visualise the scene in real-time through a head mounted display, providing him the experience of a real machine operator. Driving the harvester in a simulated forest, allowed to test the model in a hypothetical real scenario.
The process of implementing the CAD model in virtual reality used in this work, revealed to be efficient and intuitive. However, because it is a complex and large model, it was necessary to remove certain bodies (without dynamics effect) and reduce the number of contact points between components in order to balance the speed and performance of the simulator.
Following the same method used in this work, Other CAD models can be imported to virtual reality and be dynamically simulated.},
keywords = {External},
pubstate = {published},
tppubtype = {mastersthesis}
}
Computer-aided design (CAD) software is used in the product design and development to design complex and detailed prototypes. It provides good assistance and solid data generation to designers and engineers. In order to remain competitive, industry is always seeking for higher process efficiency and product quality enhancement in the shortest period of time. Continuous research keeps going to make it possible.
Virtual reality has been one of the research focus in the recent years. It is studied and applied to be used as an assistant tool in the product lifecyle management, particularly in facilitating the development phase. However, the implementation process from CAD to virtual reality remains a challenge due to time consumption and technology complexibility.
In this work a real-time virtual reality harvester simulator was developed. The start point was a 3D harvester CAD model. It was used the CAD simulator AGX Momentum, a game engine Unity
and the physics engine AGX Dynamics to create dynamics simulation, to design a virtual forest environment and to enable physical controllers interact with the model.
With the capabilities of AGX Momentum, it was added dynamics motion directly in the CAD software, creating fast CAD simulations. A virtual scene was designed with Unity to simulate an environment and the immersion of the user on it with Oculus Rift device. The harvester model was imported to the Unity scene with AGX Dynamics.
In the end it was obtained a real size virtual prototype, with the possibility of interacting and control it using physical controllers. The user can visualise the scene in real-time through a head mounted display, providing him the experience of a real machine operator. Driving the harvester in a simulated forest, allowed to test the model in a hypothetical real scenario.
The process of implementing the CAD model in virtual reality used in this work, revealed to be efficient and intuitive. However, because it is a complex and large model, it was necessary to remove certain bodies (without dynamics effect) and reduce the number of contact points between components in order to balance the speed and performance of the simulator.
Following the same method used in this work, Other CAD models can be imported to virtual reality and be dynamically simulated. |
 | Thoeni, Klaus; Servin, Martin; Sloan, Scott W; Giacomini, Anna: Designing waste rock barriers by advanced numerical modelling. In: Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 3, pp. 659-675, 2019, ISSN: 1674-7755. @article{THOENI2019659,
title = {Designing waste rock barriers by advanced numerical modelling},
author = {Klaus Thoeni and Martin Servin and Scott W Sloan and Anna Giacomini},
url = {https://www.sciencedirect.com/science/article/pii/S1674775518303895
https://www.sciencedirect.com/science/article/pii/S1674775518303895?via%3Dihub#appsec1
http://umit.cs.umu.se/wiki/Designing_waste_rock_barriers_by_advanced_numerical_modelling
},
doi = {10.1016/j.jrmge.2018.11.005},
issn = {1674-7755},
year = {2019},
date = {2019-01-01},
journal = {Journal of Rock Mechanics and Geotechnical Engineering},
volume = {11},
number = {3},
pages = {659-675},
abstract = {Design of waste rock barriers forming safety berms for haul trucks requires knowledge of complex interactions which cannot readily be tested by physical means. An advanced numerical model based on non-smooth multi-domain mechanics is presented together with model calibration using limited full-scale experimental data. Waste rock is represented by spherical particles with rolling resistance, and an ultra-class haul truck is represented by a rigid multibody system interconnected with mechanical joints. The model components are first calibrated and then the calibrated model is used for simulating various collision scenarios with different approach conditions and safety berm geometries. Numerical predictions indicate that the width of the berm is most critical for efficiently stopping a runaway truck. The model can also predict if a certain berm geometry is capable of stopping a runaway truck. Results are summarised in a series of diagrams intended for use as design guidelines by practitioners and engineers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Design of waste rock barriers forming safety berms for haul trucks requires knowledge of complex interactions which cannot readily be tested by physical means. An advanced numerical model based on non-smooth multi-domain mechanics is presented together with model calibration using limited full-scale experimental data. Waste rock is represented by spherical particles with rolling resistance, and an ultra-class haul truck is represented by a rigid multibody system interconnected with mechanical joints. The model components are first calibrated and then the calibrated model is used for simulating various collision scenarios with different approach conditions and safety berm geometries. Numerical predictions indicate that the width of the berm is most critical for efficiently stopping a runaway truck. The model can also predict if a certain berm geometry is capable of stopping a runaway truck. Results are summarised in a series of diagrams intended for use as design guidelines by practitioners and engineers. |
 | Syrén, Ludvig: A method for introducing flexibility in rigid multibodies from reduced order elastic models. Department of Physics, Umeå University, 2019. @mastersthesis{Syren2019,
title = {A method for introducing flexibility in rigid multibodies from reduced order elastic models},
author = {Ludvig Syrén},
url = {http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Aumu%3Adiva-160417
https://umu.diva-portal.org/smash/get/diva2:1326569/FULLTEXT01.pdf
},
year = {2019},
date = {2019-01-01},
school = {Department of Physics, Umeå University},
abstract = {In multibody dynamics simulation of robots and vehicles it is common to model the systems as being composed of mainly rigid bodies with articulation joints. With the trend to more lightweight robots, however, the structural flexibility of the robots link’s needs to be considered for realistic dynamic simulations. The link’s geometries are complex and finite element models (FEM) are required to compute the deformations. However, FEM includes too many degrees of freedom for time-efficient dynamics simulation. A popular method is to generate reduced order models from the FE models, but with much fewer degrees of freedom, for fast and precise simulations. In this thesis a method for introducing reduced order models in rigid multibody systems was developed. The method is to divide a rigid body into two rigid bodies. Their relative movement is described by a six degree of freedom restoration force, determined with a reduced order model from Guyan reduction (static condensation). The method was validated for quasistatic deformation of a homogenous beam, a robot link arm with a more complex geometry and in multibody dynamics simulations. Finally the method was tested in simulation of a complete ABB robot with joint actuators, and any significant differences in the motion of the robot tool centre point due to replacing a rigid link arm by a flexible one was demonstrated.The method show good results for computing deformations of the homogenous beam, of the link arm and in the multibody simulation. The differences observed in simulation of a complete robot was expected and demonstrated the method to be applicable in robotic simulations.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {mastersthesis}
}
In multibody dynamics simulation of robots and vehicles it is common to model the systems as being composed of mainly rigid bodies with articulation joints. With the trend to more lightweight robots, however, the structural flexibility of the robots link’s needs to be considered for realistic dynamic simulations. The link’s geometries are complex and finite element models (FEM) are required to compute the deformations. However, FEM includes too many degrees of freedom for time-efficient dynamics simulation. A popular method is to generate reduced order models from the FE models, but with much fewer degrees of freedom, for fast and precise simulations. In this thesis a method for introducing reduced order models in rigid multibody systems was developed. The method is to divide a rigid body into two rigid bodies. Their relative movement is described by a six degree of freedom restoration force, determined with a reduced order model from Guyan reduction (static condensation). The method was validated for quasistatic deformation of a homogenous beam, a robot link arm with a more complex geometry and in multibody dynamics simulations. Finally the method was tested in simulation of a complete ABB robot with joint actuators, and any significant differences in the motion of the robot tool centre point due to replacing a rigid link arm by a flexible one was demonstrated.The method show good results for computing deformations of the homogenous beam, of the link arm and in the multibody simulation. The differences observed in simulation of a complete robot was expected and demonstrated the method to be applicable in robotic simulations. |
2018
|
 | Lundkvist, Anna: Fatigue analysis - local geometry optimization. 2018. @mastersthesis{Lundkvist2018,
title = {Fatigue analysis - local geometry optimization},
author = {Anna Lundkvist},
url = {http://www.diva-portal.org/smash/get/diva2:1251131/FULLTEXT01.pdf
http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Aumu%3Adiva-152084},
year = {2018},
date = {2018-07-09},
abstract = {The cause of fatigue failure is repeated loads, that cause cracks to appear and grow even if the loads are far below the static load that would make a structure fail. High-cycle fatigue, which this project will focus on, is characterized by linear elastic stress and only fails after a large amount of loading cycles. While fatigue is the most common cause of failure in structures, it is not feasible to calculate fatigue damage analytically. The aim of this project was to develop, implement and test a workflow that unifies the wide range of physical scales and transient features that are relevant to fatigue analysis of complex dynamic machinery. The workflow should take both system-level and local aspects into account. The goal was to address both the global and local while still keeping practical feasibility and simulation performance in mind. The resulting unified fatigue analysis method was then used on several test cases and illustrated from a local geometry optimization perspective.
The workflow contains the following steps: First the model is to be simulated in order to get the load history. Then the finite element method (FEM) is used to make a submodel of the component that is to be analyzed. The submodel is subjected to forces and moments, and then the stress is extracted from the areas of interest in the model. Thus, a linear relation for the stress can be calculated. The stress history is calculated by putting the load history into the stress relation. Using established fatigue analysis methods like rainflow counting and the Palmgren-Miner rule the fatigue life is then calculated.
This project only had its focus on the FEM submodel part of the fatigue workflow. The geometry of the submodel should then be able to be optimized for the longest fatigue life.
The workflow was tested on several test cases. The first one was a simple upsidedown T-shaped component that was made by welding the parts together. The same component but with no weld, as if it had been moulded, was the next case. The final case was a component from the base of a crane. Two things were analyzed on this case. The first was the fatigue life around a hole in which a shaft was welded. The other was optimizing to find the best position for a hook to be welded onto the surface on the component.
},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {mastersthesis}
}
The cause of fatigue failure is repeated loads, that cause cracks to appear and grow even if the loads are far below the static load that would make a structure fail. High-cycle fatigue, which this project will focus on, is characterized by linear elastic stress and only fails after a large amount of loading cycles. While fatigue is the most common cause of failure in structures, it is not feasible to calculate fatigue damage analytically. The aim of this project was to develop, implement and test a workflow that unifies the wide range of physical scales and transient features that are relevant to fatigue analysis of complex dynamic machinery. The workflow should take both system-level and local aspects into account. The goal was to address both the global and local while still keeping practical feasibility and simulation performance in mind. The resulting unified fatigue analysis method was then used on several test cases and illustrated from a local geometry optimization perspective.
The workflow contains the following steps: First the model is to be simulated in order to get the load history. Then the finite element method (FEM) is used to make a submodel of the component that is to be analyzed. The submodel is subjected to forces and moments, and then the stress is extracted from the areas of interest in the model. Thus, a linear relation for the stress can be calculated. The stress history is calculated by putting the load history into the stress relation. Using established fatigue analysis methods like rainflow counting and the Palmgren-Miner rule the fatigue life is then calculated.
This project only had its focus on the FEM submodel part of the fatigue workflow. The geometry of the submodel should then be able to be optimized for the longest fatigue life.
The workflow was tested on several test cases. The first one was a simple upsidedown T-shaped component that was made by welding the parts together. The same component but with no weld, as if it had been moulded, was the next case. The final case was a component from the base of a crane. Two things were analyzed on this case. The first was the fatigue life around a hole in which a shaft was welded. The other was optimizing to find the best position for a hook to be welded onto the surface on the component.
|
 | Lacoursière, Claude; Härdin, Tomas: FMIGo! A runtime environment for FMI based simulation.. In: 2018. @article{lacoursierefmigo,
title = {FMIGo! A runtime environment for FMI based simulation.},
author = {Claude Lacoursière and Tomas Härdin},
url = {https://www.fmigo.net/
https://github.com/fmi-tools/FMIGo
https://webapps.cs.umu.se/uminf/reports/2018/003/part1.pdf},
year = {2018},
date = {2018-01-01},
abstract = {We present the software architecture of FMIGo!, a distributed, parallel runtime environment for executing Functional Mockup Interface (FMI) based simulations, describe how to use it, and how to extend its functionality. FMI is a standard used to define what Functional Mockup Units (FMU)s – or modules – can expose in terms of inputs and outputs, and the proper execution or call sequence one can perform on said FMUs. The FMI does not define the mathematics of modular time integration, a problem addressed by FMIGo! in an extensible way. Also included are some theoretical and experimental aspects of different stepping schemes, or numerical methods to simulate coupled, modular systems. In particular, we present a kinematic solver which corresponds to Differential Algebraic Conditions between modules, as well as wrapper FMUs designed to augment the functionality of a given FMU with, for instance, input and output filters and directional derivatives, features rarely present in modules. FMIGo! is open source under the MIT license and is meant to implement only the functionality required by steppers, as well as different stepping schemes. },
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
We present the software architecture of FMIGo!, a distributed, parallel runtime environment for executing Functional Mockup Interface (FMI) based simulations, describe how to use it, and how to extend its functionality. FMI is a standard used to define what Functional Mockup Units (FMU)s – or modules – can expose in terms of inputs and outputs, and the proper execution or call sequence one can perform on said FMUs. The FMI does not define the mathematics of modular time integration, a problem addressed by FMIGo! in an extensible way. Also included are some theoretical and experimental aspects of different stepping schemes, or numerical methods to simulate coupled, modular systems. In particular, we present a kinematic solver which corresponds to Differential Algebraic Conditions between modules, as well as wrapper FMUs designed to augment the functionality of a given FMU with, for instance, input and output filters and directional derivatives, features rarely present in modules. FMIGo! is open source under the MIT license and is meant to implement only the functionality required by steppers, as well as different stepping schemes. |
 | Auris, Felix; Zipper, Holger; Brandl, Michael; Süss, Sebastian; Diedrich, Christian: Durchgängige Nutzung von Anlagenmodellen: Über den gesamten Lebenszyklus von Produktionsanlagen. In: atp magazin, vol. 60, no. 06-07, pp. 90–91, 2018. @article{auris2018durchgangige,
title = {Durchgängige Nutzung von Anlagenmodellen: Über den gesamten Lebenszyklus von Produktionsanlagen},
author = {Felix Auris and Holger Zipper and Michael Brandl and Sebastian Süss and Christian Diedrich},
doi = {10.17560/atp.v60i06-07.2350},
year = {2018},
date = {2018-01-01},
journal = {atp magazin},
volume = {60},
number = {06-07},
pages = {90--91},
abstract = {Der Beitrag konzentriert sich auf neue Konzepte zur durchgängigen, ganzheitlichen, standardisierten und effizienten Simulation über den Anlagenlebenszyklus automatisierter Montageanlagen im Automobilbau. Es wird gezeigt, wie durch die Nutzung standardisierter Datenformate ein sogenanntes mechatronisches Anlagenmodell, oft auch als digitaler Zwilling bezeichnet, implizit aus den Konstruktionsdaten generiert werden kann und in allen folgenden Phasen des Anlagenlebenszyklus weiter-
verwendet wird.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}
Der Beitrag konzentriert sich auf neue Konzepte zur durchgängigen, ganzheitlichen, standardisierten und effizienten Simulation über den Anlagenlebenszyklus automatisierter Montageanlagen im Automobilbau. Es wird gezeigt, wie durch die Nutzung standardisierter Datenformate ein sogenanntes mechatronisches Anlagenmodell, oft auch als digitaler Zwilling bezeichnet, implizit aus den Konstruktionsdaten generiert werden kann und in allen folgenden Phasen des Anlagenlebenszyklus weiter-
verwendet wird. |
