Publications

@conference{Aoshima2021,
title = {Simulation-Based Optimization of High-Performance Wheel Loading},
author = {Koji Aoshima and Martin Servin and Eddie Wadbro},
editor = {Feng, Chen and Linner, Thomas and Brilakis, Ioannis and Castro, Daniel and Chen, Po-Han and Cho, Yong and Du, Jing and Ergan, Semiha and Garcia de Soto, Borja and Gašparík, Jozef and Habbal, Firas and Hammad, Amin and Iturralde, Kepa and Bock, Thomas and Kwon, Soonwook and Lafhaj, Zoubeir and Li, Nan and Liang, Ci-Jyun and Mantha, Bharadwaj and Ng, Ming Shan and Hall, Daniel and Pan, Mi and Pan, Wei and Rahimian, Farzad and Raphael, Benny and Sattineni, Anoop and Schlette, Christian and Shabtai, Isaac and Shen, Xuesong and Tang, Pingbo and Teizer, Jochen and Turkan, Yelda and Valero, Enrique and Zhu, Zhenhua},
url = {https://www.iaarc.org/publications/2021_proceedings_of_the_38th_isarc/simulation_based_optimization_of_high_performance_wheel_loading.html
https://arxiv.org/abs/2107.14615
http://umit.cs.umu.se/hp_loading/},
doi = {10.22260/ISARC2021/009310.22260/ISARC2021/0093},
isbn = {978-952-69524-1-3},
year = {2021},
date = {2021-08-02},
booktitle = {Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)},
pages = {688-695},
publisher = {International Association for Automation and Robotics in Construction (IAARC)},
abstract = {Having smart and autonomous earthmoving in mind, we explore high-performance wheel loading in a simulated environment. This paper introduces a wheel loader simulator that combines contacting 3D multibody dynamics with a hybrid continuum-particle terrain model, supporting realistic digging forces and soil displacements at real-time performance. A total of 270,000 simulations are run with different loading actions, pile slopes, and soil to analyze how they affect the loading performance. The results suggest that the preferred digging actions should preserve and exploit a steep pile slope. High digging speed favors high productivity, while energy-efficient loading requires a lower dig speed.},
howpublished = {38th International Symposium on Automation and Robotics in Construction (ISARC), Dubai, UAE (2021). arXiv:2107.14615 },
keywords = {Algoryx, External},
pubstate = {published},
tppubtype = {conference}
}

@conference{servintowards,
title = {Towards a graph neural network solver for granular dynamics},
author = {Martin Servin and Holger Götz and Tomas Berglund and Erik Wallin},
url = {https://www.researchgate.net/profile/Martin-Servin/publication/350089857_Towards_a_graph_neural_network_solver_for_granular_dynamics/links/6050749892851cd8ce445ca6/Towards-a-graph-neural-network-solver-for-granular-dynamics.pdf},
year = {2021},
date = {2021-03-01},
booktitle = {VII International Conference on Particle-Based Methods (PARTICLES 2021)},
abstract = {The discrete element method (DEM) is a versatile but computationally intensive method for granular dynamics simulation. We investigate the possibility of accelerating DEM simulations using graph neural networks (GNN), which automatically support variable connectivity between particles. This approach was recently found promising for particle-based simulation of complex fluids [1]. We start from a time-implicit, or nonsmooth, DEM [2], where the computational bottleneck is the process of solving a mixed linear complementarity problem (MLCP) to obtain the contact forces and particle velocity update. This solve step is substituted by a GNN, trained to predict the MLCP solution. Following [1], we employ an encoder-process-decoder structure for the GNN. The particle and connectivity data is encoded in an input graph with particle mass, external force, and previous velocity as node attributes, and contact overlap, normal, and tangent vectors as edge attributes. The sought solution is represented in the output graph with the updated particle velocities as node attributes and the contact forces as edge attributes. In the intermediate processing step, the input graph is converted to a latent graph, which is then advanced with a fixed number of message passing steps involving a multilayer perceptron neural network for updating the edge and node values. The output graph, with the approximate solution to the MLCP, is finally computed by decoding the last processed latent graph. Both a supervised and unsupervised method are tested for training the network on granular simulation of particles in a rotating or static drum. AGX Dynamics [3] is used for running the simulations, and Pytorch [4] in combination with the Deep Graph Library [5] for the learning. The supervised model learns from ground truth MLCP solutions, computed using a projected Gauss-Seidel (PGS) solver, sampled from 1200 simulations involving 50-150 particles. The unsupervised model learns to minimize a loss function derived from the MLCP residual function using particle configurations extracted from the same simulations but ignoring the approximate solution from the PGS solver. The simulation samples are split into training data (80%), validation data (10%), and test data (10%). Network hyperparameter optimization is performed. The supervised GNN solver reaches an error level of 1% for the contact forces and 0.01% on the particle velocities for a static drum. For a rotating drum, the respective errors are 10% and 1%. The unsupervised GNN solver reaches 1% velocity errors, 5% normal forces errors, but it has significant problems with predicting the friction forces. The latter is presumably because of the discontinuous loss function that follows from the Coulomb friction law and therefore we explore regularization of it. Finally, we discuss the potential scalability and performance for large particle systems.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {conference}
}

@conference{laezzashape2020,
title = {Shape Control of Elastoplastic Deformable Linear Objects through Reinforcement Learning},
author = {Rita Laezza and Yiannis Karayiannidis},
url = {https://ras.papercept.net/proceedings/IROS20/3496.pdf},
year = {2020},
date = {2020-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Las Vegas (Virtual), USA, 2020-10-25 - 2020-10-29},
abstract = {Deformable object manipulation tasks have longbeen regarded as challenging robotic problems. However, untilrecently, very little work had been done on the subject, withmost robotic manipulation methods being developed for rigidobjects. As machine learning methods are becoming morepowerful, there are new model-free strategies to explore forthese objects, which are notoriously hard to model. This paperfocuses on shape control problems for Deformable Linear Objects (DLOs). Despite being one of the most researched classesof DLOs in terms of geometry, no other paper has focusedon materials with elastoplastic properties. Therefore, a novelshape control task, requiring permanent plastic deformationis implemented in a simulation environment. ReinforcementLearning methods are used to learn a continuous controlpolicy. To that end, a discrete curvature measure is usedas a low-dimensional state representation and as part of anintuitive reward function. Finally, three state-of-the-art actor-critic algorithms are compared on the proposed environmentand successfully achieve the goal shape.},
keywords = {External},
pubstate = {published},
tppubtype = {conference}
}

@conference{berglund2018virtual,
title = {Virtual commissioning of a mobile ore chute},
author = {Tomas Berglund and Kjell-Ove Mickelsson and Martin Servin},
url = {http://umit.cs.umu.se/modsimcomplmech/docs/papers/vcmoc_chops.pdf
http://umit.cs.umu.se/chute/
https://www.linkedin.com/pulse/virtual-commissioning-mobile-ore-chute-martin-servin/
},
year = {2018},
date = {2018-01-01},
booktitle = {The 9nth International Conference on Conveying and Handling of Particulate Solids (CHoPS), London, UK (2018)},
journal = {simulation},
volume = {10},
pages = {14th},
abstract = {This paper describes the virtual commissioning of a mobile ore chute for sequential loading of trucks from a conveyor system with a continuous material flow. The design and control were tested in simulation environment and improved prior to its installation in an underground mine in full production. The altered design met the performance goal and the amount of rock spill and wear on surrounding equipment could be reduced significantly. The simulations were based on a novel combination of discrete element and multibody simulation using a nonsmooth dynamics formulation, integrated in a 3D modeling software. This enable both fast simulation, based on original CAD drawings, and high flexibility in modifying the design and control.},
keywords = {Algoryx},
pubstate = {published},
tppubtype = {conference}
}