Commenced in January 2007
Paper Count: 32451
Simulation of Obstacle Avoidance for Multiple Autonomous Vehicles in a Dynamic Environment Using Q-Learning
Authors: Andreas D. Jansson
Abstract:The availability of inexpensive, yet competent hardware allows for increased level of automation and self-optimization in the context of Industry 4.0. However, such agents require high quality information about their surroundings along with a robust strategy for collision avoidance, as they may cause expensive damage to equipment or other agents otherwise. Manually defining a strategy to cover all possibilities is both time-consuming and counter-productive given the capabilities of modern hardware. This paper explores the idea of a model-free self-optimizing obstacle avoidance strategy for multiple autonomous agents in a simulated dynamic environment using the Q-learning algorithm. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 378
 G. G. Meyer, K. Främling, and J. Holmström, “Intelligent products: a survey”, Computers in Industry, vol. 60, issue 3, pp. 137-148, Apr. 2009.
 T. Skjoett-Larsen, “European logistics beyond 2000”, International Journal of Physical Distribution & Logistics Management, vol. 30, issue 5, pp. 377-387, June 2000.
 R. Sella, A. Rassõlkinb, R. Wanga, and T. Otto, “Integration of autonomous vehicles and industry 4.0”, in Proceedings of the Estonian Academy of Sciences, Tallin, Estonia, 2019, pp. 389–394.
 Y. Cheng, and G. Y. Wang, “Mobile robot navigation based on lidar”, 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China, 2018, pp. 1243-1246.
 F. B. P. Malavazi, R. Guyonneau, J.-B Fasquel, S. Lagrange, and F. Mercier, “LiDAR-only based navigation algorithm for an autonomous agricultural robot”, Computers and Electronics in Agriculture, vol. 154, pp. 71-79, Nov. 2018.
 B Z. Su, X. Zhou, T. Cheng, H. Zhang, B. Xu, and W. Chen, "Global localization of a mobile robot using lidar and visual features," 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, Macao, 2017, pp. 2377-2383.
 Z. Yan, L. Sun, T. Duckctr, and N. Bellotto, "Multisensor online transfer learning for 3D LiDAR-based human detection with a mobile robot," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018, pp. 7635-7640.
 M. Freese, S. Singh., F. Ozaki, and N. Matsuhira, “Virtual robot experimentation platform V-REP: a versatile 3D robot simulator” in Simulation, Modeling, and Programming for Autonomous Robots, N. Ando, S. Balakirsky, T. Hemker, M. Reggiani, and O. von Stryk, Ed. Berlin Heidelberg: Springer-Verlag, 2010, pp. 51-62.
 S. Nakaoka, "Choreonoid: Extensible virtual robot environment built on an integrated GUI framework," 2012 IEEE/SICE International Symposium on System Integration (SII), Fukuoka, Japan, 2012, pp. 79-85.
 R. S. Sutton, and A. G. Barto, Reinforcement learning: an introduction, 2nd edition. Cambridge, MA: The MIT Press, 2015, ch. 6.5.