Publications

@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://arxiv.org/pdf/2104.09111.pdf
https://arxiv.org/abs/2104.09111
},
year = {2021},
date = {2021-04-19},
journal = {arXiv:2104.09111},
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{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},
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 = {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 = {arXiv preprint 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://arxiv.org/abs/2103.01283
https://arxiv.org/pdf/2103.01283
https://www.algoryx.se/papers/drl-loader/
https://youtu.be/RzDTFZW26H0},
year = {2021},
date = {2021-01-01},
journal = {arXiv preprint arXiv:2103.01283},
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{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
},
year = {2021},
date = {2021-01-01},
journal = {arXiv preprint arXiv:2104.02004},
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{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{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}
}

@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}
}

@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}
}

@article{nordberg2018particle,
title = {Particle-based solid for nonsmooth multidomain dynamics},
author = {John Nordberg and Martin Servin},
url = {http://umit.cs.umu.se/modsimcomplmech/docs/papers/elastoplastic.pdf
http://umit.cs.umu.se/elastoplastic/
https://vimeo.com/140474641},
doi = {10.1007/s40571-017-0158-3},
year = {2018},
date = {2018-01-01},
journal = {Computational Particle Mechanics},
volume = {5},
number = {2},
pages = {125--139},
publisher = {Springer International Publishing},
abstract = {A method for simulation of elastoplastic solids in multibody systems with nonsmooth and multidomain dynamics is developed. The solid is discretised into pseudo-particles using the meshfree moving least squares method for computing the strain tensor. The particle’s strain and stress tensor variables are mapped to a compliant deformation constraint. The discretised solid model thus fit a unified framework for nonsmooth multidomain dynamics simulations including rigid multibodies with complex kinematic constraints such as articulation joints, unilateral contacts with dry friction, drivelines, and hydraulics. The nonsmooth formulation allows for impact impulses to propagate instantly between the rigid multibody and the solid. Plasticity is introduced through an associative perfectly plastic modified Drucker–Prager model. The elastic and plastic dynamics are verified for simple test systems, and the capability of simulating tracked terrain vehicles driving on a deformable terrain is demonstrated.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}

@article{lindmark2018computational,
title = {Computational exploration of robotic rock loading},
author = {Daniel M Lindmark and Martin Servin},
url = {http://umit.cs.umu.se/modsimcomplmech/docs/papers/computational_exploration.pdf
http://umit.cs.umu.se/loading/
https://vimeo.com/230986207},
doi = {10.1016/j.robot.2018.04.010},
year = {2018},
date = {2018-01-01},
journal = {Robotics and Autonomous Systems},
volume = {106},
pages = {117--129},
publisher = {North-Holland},
abstract = {A method for simulation-based development of robotic rock loading systems is described and tested. The idea is to first formulate a generic loading strategy as a function of the shape of the rock pile, the kinematics of the machine and a set of motion design variables that will be used by the autonomous control system. The relation between the loading strategy and resulting performance is then explored systematically using contacting multibody dynamics simulation, multiobjective optimisation and surrogate modelling. With the surrogate model it is possible to find Pareto optimal loading strategies for dig plans that are adapted to the current shape of the pile. The method is tested on a load–haul–dump machine loading from a large muck pile in an underground mine, with the loading performance measured by productivity, machine wear and rock debris spill that cause interruptions.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}

@article{gregorcic2017algodoo,
title = {Algodoo: A tool for encouraging creativity in physics teaching and learning},
author = {Bor Gregorcic and Madelen Bodin},
url = {https://www.researchgate.net/profile/Bor-Gregorcic/publication/312021107_Algodoo_A_Tool_for_Encouraging_Creativity_in_Physics_Teaching_and_Learning/links/5b6de42f45851546c9fa3f60/Algodoo-A-Tool-for-Encouraging-Creativity-in-Physics-Teaching-and-Learning.pdf},
doi = {10.1119/1.4972493},
year = {2017},
date = {2017-01-01},
journal = {The Physics Teacher},
volume = {55},
number = {1},
pages = {25--28},
publisher = {American Association of Physics Teachers},
abstract = {Algodoo (http://www.algodoo.com) is a digital sandbox for physics 2D simulations. It allows students and teachers to easily create simulated “scenes” and explore physics through a user-friendly and visually attractive interface. In this paper, we present different ways in which students and teachers can use Algodoo to visualize and solve physics problems, investigate phenomena and processes, and engage in out-of-school activities and projects. Algodoo, with its approachable interface, inhabits a middle ground between computer games and “serious” computer modeling. It is suitable as an entry-level modeling tool for students of all ages and can facilitate discussions about the role of computer modeling in physics.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {article}
}