Publications

@article{yang2022,
title = {Online Model Learning for Shape Control of Deformable Linear Objects},
author = {Yuxuan Yang and Johannes A. Stork and Todor Stoyanov},
url = {https://ieeexplore.ieee.org/document/9981080},
doi = {10.1109/IROS47612.2022.9981080},
year = {2022},
date = {2022-12-26},
journal = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages = {4056-4062},
abstract = {Traditional approaches to manipulating the state of deformable linear objects (DLOs) - i.e., cables, ropes - rely on model-based planning. However, constructing an accurate dynamic model of a DLO is challenging due to the complexity of interactions and a high number of degrees of freedom. This renders the task of achieving a desired DLO shape particularly difficult and motivates the use of model-free alternatives, which while maintaining generality suffer from a high sample complexity. In this paper, we bridge the gap between these fundamentally different approaches and propose a framework that learns dynamic models of DLOs through trial-and-error interaction. Akin to model-based reinforcement learning (RL), we interleave learning and exploration to solve a 3D shape control task for a DLO. Our approach requires only a fraction of the interaction samples of the current state-of-the-art model-free RL alternatives to achieve superior shape control performance. Unlike offline model learning, our approach does not require expert knowledge for data collection, retains the ability to explore, and automatically selects relevant experience.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}

@article{cosgunimpact,
title = {Impact-aware dual-arm grasping through time-invariant reference spreading},
author = {Cosgun, A and van Steen},
url = {https://pure.tue.nl/ws/portalfiles/portal/224201404/1020574_Impact_aware_grasping_through_time_invariant_reference_spreading.pdf},
year = {2022},
date = {2022-10-01},
abstract = {Eindhoven University of Technology},
abstract = {In robotic applications which involve handling objects or interaction with the environment,
robots generally establish contact at near-zero speed due to the effect of impacts. At impact, rapid velocity changes occur in the robot joints accompanied by large contact forces,
which can cause damage to the robot, humans, or the environment. As a result, robots
typically accelerate and decelerate repeatedly to achieve a task that involves contact, which
negatively affects the energy consumption and throughput. In order to increase the performance, control strategies are proposed that take into account the impact dynamics, which
are referred to as impact-aware control. Impact-aware control strategies exploit impacts, to
increase the swiftness of the performed tasks and reach a more human-like behavior. One
of the challenges in impact-aware control strategies is that, in presence of uncertainties, a
robot can experience an impact at a different time than expected. This causes the actual
state of the system to reside in a different mode than prescribed by the time-based reference
trajectory, which can cause the robot to compensate aggressively and possibly becoming unstable. Furthermore, when contact is expected to be established at multiple contact points
simultaneously, the controller can enter an unspecified and typically unpredictable mode.
Lastly, in case of external perturbations, the robot can lag behind compared to its reference
state at that time instant, which would cause the robot to “catch up” in order to reach its
desired state, which would generally be undesired.

The combination of a time-invariant control strategies and an impact-aware reference spreading control strategy can provide a possible solution for these challenges. By employing a reference that is solely a function of the robot’s position and orientation, and extending it around
an expected impact position, robots can be controlled towards an intentional impact in the
presence of uncertainties. Therefore, the goal of this project is to design an impact-aware
time-invariant reference spreading control strategy for a dual-arm robotic system capable
of swift grasping. To achieve this, a procedure is proposed to generate time-invariant references that can steer the robots towards a desired position and speed to swiftly grasp an
object, while ensuring the reference is compatible through the impact dynamics. In order
to be applicable on a dual-arm robotic system, the time-invariant reference spreading control strategy is cast into a task-based quadratic-programming control framework, which is
a common framework for control of complex systems. Furthermore, an intermediate mode
control strategy is proposed to ensure full contact is established when a loss of simultaneity
occurs. Additionally, a synchronization strategy is formulated to ensure the robots reach
the object at the same time. Finally, the effectiveness of the proposed control approach is
validated by means of numerical simulation studies on a two-dimensional, and a more complex three-dimensional scenario, which consists of a realistic seven degrees of freedom robot
setup.},
keywords = {External},
pubstate = {published},
tppubtype = {article}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}