Publications

@article{nokey,

title = {Simulated installation of a breeding blanket segment in a two-port mover context},

author = {Petri Tikka and Hannu Saarinen and Hannu Martikainen and William Brace and Janne Lyytinen},

url = {https://www.sciencedirect.com/science/article/pii/S0920379625001899},

year  = {2025},

date = {2025-03-25},

urldate = {2025-03-25},

journal = {Fusion Engineering and Design},

volume = {215},

number = {114989},

abstract = {The abstract relates to work undertaken on the Two Port Mover (TPM) Remote Maintenance (RM) concept in European DEMO. The focus of the study was to assess the operational procedures for installing the Blanket Leg Unit and Lower Port Winch of the Two-Port Mover. In addition, alternative means for constraining the rotations of the breeding blanket during the removal and installation process of blankets were studied.

The Two-Port Mover was tested in a dynamic simulation. The assessment process was carried out by using a Unity game-engine in a combination of AGX Dynamics for Unity plugin. The scope was to utilize virtual prototyping to visualize how the cabling of the studied Lower Port winch approach would interact within the Vacuum Vessel sector. Dynamic physics enables the cabling and the interface plate to collide with the surrounding environment and proper adjustments to the performed procedures can be validated already in the early stages of the design process. The final connection with the interface plate and the winches is performed by a remotely operated manipulator in a virtual environment. In the context of this simulation, the manipulator was controlled with a haptic device. This ensured precise control of the connection process, but also demonstrated how the cables would interact while operating the manipulator. Thus, influencing initial TPM design maturity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {E-GCDT: advanced reinforcement learning with GAN-enhanced data for continuous excavation system},

author = {Qianyou Zhao and Le Gao and Duidi Wu and Yihao Lei and Lingyu Wang and Jin Qi and Jie Hu},

url = {https://rdcu.be/d9k0a},

doi = {https://doi.org/10.1007/s10489-025-06308-5},

year  = {2025},

date = {2025-01-28},

urldate = {2025-01-28},

journal = {Applied Intelligence},

volume = {55},

number = {413},

abstract = {The automation of excavator operations entails the development and implementation of systems that allow excavators to execute tasks autonomously, thereby significantly reducing the need for human intervention. By integrating advanced sensors and artificial intelligence algorithms, these systems aim to increase operational efficiency, safety, and precision in construction and mining. However, previously developed methods have two weaknesses. First, existing automated excavator systems struggle with adapting to diverse and complex environmental conditions and with precision in control mechanisms. Second, operating an excavator involves multiple, repeated decisions that need to be modeled, planned, and executed in real time. However, there is a significant lack of comprehensive datasets that reflect real-world excavation operations to support this process. In this paper, we present an innovative system named E-GCDT. This system integrates the DoppelGANger module, which generates action time series by emulating human-mined trajectories through a generative adversarial mechanism and replays them in a simulation environment, ultimately expanding the dataset to 155 continuous mining trajectories. Furthermore, E-GCDT integrates terrain features into the decision-making process with the contrastive language-image pre-training model (CLIP), in which the decision transformer optimizes trajectory planning for efficient and accurate continuous excavation tasks. E-GCDT uniquely integrates advanced data augmentation and terrain awareness, developing an advanced Markov decision framework (DT) for continuous excavation tasks. The experimental results of a bulldozer verify that the efficiency of E-GCDT surpasses human efficiency. This system sets a new standard for continuous autonomous mining, and this study provides a new perspective on the application of reinforcement learning in industrial environments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Local particle refinement in terramechanical simulations},

author = {Markus Pogilus and Martin Servin},

url = {https://arxiv.org/abs/2501.05300

https://arxiv.org/pdf/2501.05300},

year  = {2025},

date = {2025-01-10},

urldate = {2025-01-10},

journal = {arxiv:2501.05300},

abstract = {The discrete element method (DEM) is a powerful tool for simulating granular soils, but its high computational demand often results in extended simulation times. While the effect of particle size has been extensively studied, the potential benefits of spatially scaling particle sizes are less explored. In this study, we systematically investigate a local particle refinement method's impact on reducing computational effort while maintaining accuracy. We first conduct triaxial tests to verify that bulk mechanical properties are preserved under local particle refinement. Then, we perform pressure-sinkage and shear-displacement tests, comparing our method to control simulations with homogeneous particle size. We evaluate 36 different DEM beds with varying aggressiveness in particle refinement. Our results show this approach, depending on refinement aggressiveness, can significantly reduce particle count by 2.3 to 25 times and simulation times by 3.1 to 43 times, with normalized errors ranging from 3.4% to 11%. The approach can keep a high resolution at the soil surface, where it is critical for accurately capturing the interaction while allowing larger particles below the surface. The results demonstrate that substantial computational savings can be achieved without significantly compromising simulation accuracy. This method can enhance the feasibility and efficiency of DEM simulations in terramechanics applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Thoeni2024,

title = {Edge protection along haul roads in mines and quarries: A rigorous study based on full-scale testing and numerical modelling},

author = {Klaus Thoeni and P Hartmann and Tomas Berglund and Martin Servin},

doi = {https://doi.org/10.1016/j.jrmge.2024.10.005},

isbn = {1674-7755},

year  = {2024},

date = {2024-10-11},

urldate = {2024-10-11},

journal = {Journal of Rock Mechanics and Geotechnical Engineering},

abstract = {Safety berms (also called safety bunds or windrows), widely employed in surface mining and quarry operations, are typically designed based on rules of thumb. Despite having been used by the industry for more than half a century and accidents happening regularly, their behaviour is still poorly understood. This paper challenges existing practices through a comprehensive investigation combining full-scale experiments and advanced numerical modelling. Focusing on a Volvo A45G articulated dump truck (ADT) and a CAT 773B rigid dump truck (RDT), collision scenarios under various approach conditions and different safety berm geometries and materials are rigorously examined. The calibrated numerical model is used to assess the energy absorption capacity of safety berms with different geometry and to predict a critical velocity for a specific scenario. Back analysis of an actual fatal accident indicated that an ADT adhering to the speed limit could not be stopped by the safety berm designed under current guidelines. The study highlights the importance of considering the entire geometry and the mass and volume of the material used to build the safety berm alongside the speed and approach conditions of the machinery. The findings of the study enable operators to set speed limits based on specific berm geometries or adapt safety berm designs to match speed constraints for specific machinery. This will reduce the risk of fatal accidents and improve haul road safety.},

keywords = {Algoryx, External},

pubstate = {published},

tppubtype = {article}

}

@article{wiberg2023sim,

title = {Sim-to-real transfer of active suspension control using deep reinforcement learning},

author = {Viktor Wiberg and Erik Wallin and Arvid Fälldin and Tobias Semberg and Morgan Rossander and Eddie Wadbro and Martin Servin},

url = {https://doi.org/10.1016/j.robot.2024.104731

https://arxiv.org/abs/2306.11171

http://umit.cs.umu.se/s2r-ascdrl/},

doi = {10.1016/j.robot.2024.104731},

year  = {2024},

date = {2024-07-10},

urldate = {2024-07-10},

journal = {Robotics and Autonomous Systems},

volume = {179},

number = {104731},

abstract = {We explore sim-to-real transfer of deep reinforcement learning controllers for a heavy vehicle with active suspensions designed for traversing rough terrain. While related research primarily focuses on lightweight robots with electric motors and fast actuation, this study uses a forestry vehicle with a complex hydraulic driveline and slow actuation. We simulate the vehicle using multibody dynamics and apply system identification to find an appropriate set of simulation parameters. We then train policies in simulation using various techniques to mitigate the sim-to-real gap, including domain randomization, action delays, and a reward penalty to encourage smooth control. In reality, the policies trained with action delays and a penalty for erratic actions perform nearly at the same level as in simulation. In experiments on level ground, the motion trajectories closely overlap when turning to either side, as well as in a route tracking scenario. When faced with a ramp that requires active use of the suspensions, the simulated and real motions are in close alignment. This shows that the actuator model together with system identification yields a sufficiently accurate model of the actuators. We observe that policies trained without the additional action penalty exhibit fast switching or bang–bang control. These present smooth motions and high performance in simulation but transfer poorly to reality. We find that policies make marginal use of the local height map for perception, showing no indications of predictive planning. However, the strong transfer capabilities entail that further development concerning perception and performance can be largely confined to simulation.},

keywords = {External},

pubstate = {published},

tppubtype = {article}

}

@article{aoshima:2023:pmh,

title = {World modeling for autonomous wheel loaders},

author = {Koji Aoshima and Arvid Fälldin and Eddie Wadbro and Martin Servin},

url = {https://www.mdpi.com/2673-4052/5/3/16

https://www.mdpi.com/2673-4052/5/3/16/pdf

http://umit.cs.umu.se/wl-predictor/},

doi = {10.3390/automation5030016},

year  = {2024},

date = {2024-07-07},

urldate = {2023-10-02},

journal = {Automation},

volume = {5},

issue = {3},

pages = {259-281},

abstract = {This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs that include a heightmap of the initial pile shape and action parameters for an automatic bucket-filling controller. Long-horizon planning of sequential loading in a dynamically changing environment is thus enabled as repeated model inference. The models, consisting of deep neural networks, were trained on data from a 3D multibody dynamics simulation of over 10,000 random loading actions in gravel piles of different shapes. The accuracy and inference time for predicting the loading performance and the resulting pile state were, on average, 95% in 1.2 ms and 97% in 4.5 ms, respectively. Long-horizon predictions were found feasible over 40 sequential loading actions.},

keywords = {Algoryx, External},

pubstate = {published},

tppubtype = {article}

}

@article{aoshima2023sim2real,

title = {Examining the simulation-to-reality gap of a wheel loader digging in deformable terrain},

author = {Koji Aoshima and Martin Servin},

url = {https://doi.org/10.1007/s11044-024-10005-5

https://arxiv.org/abs/2310.05765

http://umit.cs.umu.se/wl-sim-to-real/},

doi = {doi.org/10.1007/s11044-024-10005-5},

year  = {2024},

date = {2024-06-30},

urldate = {2023-10-10},

journal = {Multibody System Dynamics},

abstract = {We investigate how well a physics-based simulator can replicate a real wheel loader performing bucket filling in a pile of soil. The comparison is made using field test time series of the vehicle motion and actuation forces, loaded mass, and total work. The vehicle was modeled as a rigid multibody system with frictional contacts, driveline, and linear actuators. For the soil, we tested discrete element models of different resolutions, with and without multiscale acceleration. The spatio-temporal resolution ranged between 50-400 mm and 2-500 ms, and the computational speed was between 1/10,000 to 5 times faster than real- time. The simulation-to-reality gap was found to be around 10% and exhibited a weak dependence on the level of fidelity, e.g., compatible with real-time simulation. Furthermore, the sensitivity of an optimized force feedback controller under transfer between different simulation domains was investigated. The domain bias was observed to cause a performance reduction of 5% despite the domain gap being about 15%.},

keywords = {Algoryx, External},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Bayesian Optimization for Digging Control of Wheel-Loader Using Robot Manipulator},

author = {Motoki Koyama and Hiroaki Muranaka and Masato Ishikawa and Yuki Takagi},

url = {https://www.jstage.jst.go.jp/article/jrobomech/36/2/36_273/_pdf},

doi = {https://doi.org/10.20965/jrm.2021.p1248},

year  = {2024},

date = {2024-04-20},

urldate = {2024-04-20},

journal = {Journal of Robotics and Mechatronics},

volume = {36},

number = {2},

pages = {273-283},

abstract = {Wheel loaders are construction machines that are mainly used for excavating and loading sedimented ground into dump trucks. The objects to be excavated range from large materials, such as blast rock and crushed stone, to small materials, such as gravel, slag, and coal ash. Therefore, the excavation operation of wheel loaders requires a high skill level to cope with various terrains and soil types. As worker numbers at quarry sites decline, developing highly automated technology to replace operators is crucial. In particular, the geometry of the ground to be excavated by the wheel loader changes with each excavation, so the control parameters must be adapted sequentially during automated excavation. In this study, we proposed an online learning method using Bayesian optimization to search for control parameters from multiple trials and modify them sequentially. In particular, we formulate a multi-objective optimization problem maximizing a weighted linear combination of the payload and workload as an objective function. To validate the proposed method, we constructed an environment in which repeated digging tests can be performed using a robot manipulator with a bucket attached. Through comparative tests between feed-forward control, in which the robot moves along a fixed trajectory independent of the digging reaction force, and off-line control, in which the robot modifies the digging trajectory in response to the digging reaction force, we compared the ability of these methods to cope with terrain volume that is different from that of the optimization trial.},

keywords = {External},

pubstate = {published},

tppubtype = {article}

}

@article{wallin2023multi,

title = {Multi-log grasping using reinforcement learning and virtual visual servoing},

author = {Erik Wallin and Viktor Wiberg and Martin Servin},

url = {https://www.mdpi.com/2218-6581/13/1/3

https://arxiv.org/abs/2309.02997

http://umit.cs.umu.se/multi-log-grasping/},

doi = {10.3390/robotics13010003},

year  = {2024},

date = {2024-01-30},

urldate = {2023-09-15},

journal = {Robotics },

volume = {13},

number = {3},

issue = {1},

abstract = {We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves problems of dynamics and path planning, where the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilize a virtual camera to provide an image stream from 3D reconstructed data. We use Cartesian control to simplify domain transfer. Since log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limit on computational resources and time for the challenge of image segmentation, and allows for collecting data in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2-5 logs.},

keywords = {External},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Multi-Arm Trajectory Planning for Optimal Collision-Free Pick-and-Place Operations},

author = {Daniel Mateu-Gomez and Francisco José Martínez-Peral and Carlos Perez-Vidal},

url = {https://www.mdpi.com/2227-7080/12/1/12#},

doi = {https://doi.org/10.3390/technologies12010012},

year  = {2024},

date = {2024-01-22},

urldate = {2024-01-22},

journal = {Technologies},

volume = {12},

number = {12},

issue = {1},

abstract = {This article addresses the problem of automating a multi-arm pick-and-place robotic system. The objective is to optimize the execution time of a task simultaneously performed by multiple robots, sharing the same workspace, and determining the order of operations to be performed. Due to its ability to address decision-making problems of all kinds, the system is modeled under the mathematical framework of the Markov Decision Process (MDP). In this particular work, the model is adjusted to a deterministic, single-agent, and fully observable system, which allows for its comparison with other resolution methods such as graph search algorithms and Planning Domain Definition Language (PDDL). The proposed approach provides three advantages: it plans the trajectory to perform the task in minimum time; it considers how to avoid collisions between robots; and it automatically generates the robot code for any robot manufacturer and any initial objects’ positions in the workspace. The result meets the objectives and is a fast and robust system that can be safely employed in a production line.},

keywords = {External},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Advanced virtual prototyping of robotic cells using physics-based simulation},

author = {Roberto Raffaeli and Federico Neri and Margherita Peruzzini and Giovanni Berselli and Marcello Pellicciari},

doi = {https://doi.org/10.1007/s12008-023-01677-y},

year  = {2023},

date = {2023-12-19},

urldate = {2023-12-19},

journal = {International Journal on Interactive Design and Manufacturing},

volume = {18},

pages = {981–996},

abstract = {Robotic cells are complex mechatronic systems whose final performance is determined by the interaction of the control logics with the mechanical behavior of the process. In this context it is fundamental to develop engineering methods and tools for the virtual prototyping of the cells that emulate both contributions. With such mechatronic digital models, it would be possible to replicate the real behavior of the systems and to optimize the cell productivity, up to building complete digital twins. This paper proposes an engineering method to develop realistic Virtual Prototypes of robotic cells including their geometry, operating logic, performance, and physical behavior. A case study on a robotic cell composed of two anthropomorphic robots for the flexible process of automatic assembly of industrial parts is presented to demonstrate the approach.},

keywords = {External},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ2023115417,

title = {Variable buoyancy anchor deployment analysis for floating wind applications using a Marine Simulator},

author = {Rodrigo Martinez and Sergi Arnau and Callum Scullion and Paddy Collins and Richard D Neilson and Marcin Kapitaniak},

url = {https://www.sciencedirect.com/science/article/pii/S0029801823018012},

doi = {https://doi.org/10.1016/j.oceaneng.2023.115417},

issn = {0029-8018},

year  = {2023},

date = {2023-07-16},

journal = {Ocean Engineering},

volume = {285},

pages = {115417},

abstract = {To study the feasibility of deploying a novel type of anchor with variable buoyancy for mooring floating offshore wind turbines, a set of detailed modelling studies was performed in the state of-the-art Marine Simulator at the National Decommissioning Centre. The aim of the multiphysics simulations is to assess fully a proposed craneless deployment method that involves towing the anchor from the harbour to the installation site, pumping liquid ballast to overcome anchor’s buoyancy and lowering it to the seabed using only a winch, thereby simplifying the process, and reducing installation costs. As a test case, a novel shape of the floating anchor is considered, to establish the feasibility of its deployment in conjunction with the variable buoyancy technology and installation sequence. The analysis is divided into three sections: characterisation of the anchor buoyancy, positioning the anchor under the stern of the vessel and the controlled descent of the anchor to the seabed, under varying weather and operational conditions (e.g significant wave height, current, winch velocity, liquid ballast mass, ballast pump rate). The analysis allows assessment of the importance of the different factors affecting the proposed deployment scenario of variable buoyancy anchors, such as the winch velocity, the ballast mass and the pump rate.},

keywords = {External},

pubstate = {published},

tppubtype = {article}

}

@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{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{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{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{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{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{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{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{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{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{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}

}