2000 character limit reached
Decentralized Lifelong Path Planning for Multiple Ackerman Car-Like Robots (2402.11767v1)
Published 19 Feb 2024 in cs.RO
Abstract: Path planning for multiple non-holonomic robots in continuous domains constitutes a difficult robotics challenge with many applications. Despite significant recent progress on the topic, computationally efficient and high-quality solutions are lacking, especially in lifelong settings where robots must continuously take on new tasks. In this work, we make it possible to extend key ideas enabling state-of-the-art (SOTA) methods for multi-robot planning in discrete domains to the motion planning of multiple Ackerman (car-like) robots in lifelong settings, yielding high-performance centralized and decentralized planners. Our planners compute trajectories that allow the robots to reach precise $SE(2)$ goal poses. The effectiveness of our methods is thoroughly evaluated and confirmed using both simulation and real-world experiments.
- P. R. Wurman, R. D’Andrea, and M. Mountz, “Coordinating hundreds of cooperative, autonomous vehicles in warehouses,” AI magazine, vol. 29, no. 1, pp. 9–9, 2008.
- R. Mason, “Developing a profitable online grocery logistics business: Exploring innovations in ordering, fulfilment, and distribution at ocado,” in Contemporary Operations and Logistics. Springer, 2019, pp. 365–383.
- Q. Wan, C. Gu, S. Sun, M. Chen, H. Huang, and X. Jia, “Lifelong multi-agent path finding in a dynamic environment,” in 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2018, pp. 875–882.
- K. Okumura, M. Machida, X. Défago, and Y. Tamura, “Priority inheritance with backtracking for iterative multi-agent path finding,” in International Joint Conference on Artificial Intelligence, 2019.
- M. Barer, G. Sharon, R. Stern, and A. Felner, “Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem,” in Seventh Annual Symposium on Combinatorial Search, 2014.
- L. Wen, Y. Liu, and H. Li, “Cl-mapf: Multi-agent path finding for car-like robots with kinematic and spatiotemporal constraints,” Robotics and Autonomous Systems, vol. 150, p. 103997, 2022.
- G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant, “Conflict-based search for optimal multi-agent pathfinding,” Artificial Intelligence, vol. 219, pp. 40–66, 2015.
- R. Stern, N. Sturtevant, A. Felner, S. Koenig, H. Ma, T. Walker, J. Li, D. Atzmon, L. Cohen, T. Kumar, et al., “Multi-agent pathfinding: Definitions, variants, and benchmarks,” arXiv preprint arXiv:1906.08291, 2019.
- P. Surynek, “An optimization variant of multi-robot path planning is intractable,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, no. 1, 2010.
- J. Yu and S. M. LaValle, “Structure and intractability of optimal multi-robot path planning on graphs,” in Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013.
- ——, “Optimal multirobot path planning on graphs: Complete algorithms and effective heuristics,” IEEE Transactions on Robotics, vol. 32, no. 5, pp. 1163–1177, 2016.
- E. Erdem, D. G. Kisa, U. Öztok, and P. Schueller, “A general formal framework for pathfinding problems with multiple agents.” in AAAI, 2013.
- G. Sharon, R. Stern, M. Goldenberg, and A. Felner, “The increasing cost tree search for optimal multi-agent pathfinding,” Artificial Intelligence, vol. 195, pp. 470–495, 2013.
- D. Silver, “Cooperative pathfinding.” AIIDE, vol. 1, pp. 117–122, 2005.
- K. Okumura, “Lacam: Search-based algorithm for quick multi-agent pathfinding,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 10, 2023, pp. 11 655–11 662.
- R. J. Luna and K. E. Bekris, “Push and swap: Fast cooperative path-finding with completeness guarantees,” in Twenty-Second International Joint Conference on Artificial Intelligence, 2011.
- T. Guo and J. Yu, “Sub-1.5 Time-Optimal Multi-Robot Path Planning on Grids in Polynomial Time,” in Proceedings of Robotics: Science and Systems, New York City, NY, USA, June 2022.
- S. D. Han and J. Yu, “Ddm: Fast near-optimal multi-robot path planning using diversified-path and optimal sub-problem solution database heuristics,” IEEE Robotics and Automation Letters, vol. 5, pp. 1350–1357, 2019.
- G. Sartoretti, J. Kerr, Y. Shi, G. Wagner, T. S. Kumar, S. Koenig, and H. Choset, “Primal: Pathfinding via reinforcement and imitation multi-agent learning,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2378–2385, 2019.
- Q. Li, F. Gama, A. Ribeiro, and A. Prorok, “Graph neural networks for decentralized multi-robot path planning,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020, pp. 11 785–11 792.
- T. Guo and J. Yu, “Toward efficient physical and algorithmic design of automated garages,” arXiv preprint arXiv:2302.01305, 2023.
- A. Okoso, K. Otaki, S. Koide, and T. Nishi, “High density automated valet parking via multi-agent path finding,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022, pp. 2146–2153.
- J. Li, M. Ran, and L. Xie, “Efficient trajectory planning for multiple non-holonomic mobile robots via prioritized trajectory optimization,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 405–412, 2020.
- D. Le and E. Plaku, “Multi-robot motion planning with dynamics via coordinated sampling-based expansion guided by multi-agent search,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1868–1875, 2019.
- Y. Ouyang, B. Li, Y. Zhang, T. Acarman, Y. Guo, and T. Zhang, “Fast and optimal trajectory planning for multiple vehicles in a nonconvex and cluttered environment: Benchmarks, methodology, and experiments,” in 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 10 746–10 752.
- J. Alonso-Mora, A. Breitenmoser, P. Beardsley, and R. Siegwart, “Reciprocal collision avoidance for multiple car-like robots,” in 2012 IEEE International Conference on Robotics and Automation. IEEE, 2012, pp. 360–366.
- J. Alonso-Mora, P. Beardsley, and R. Siegwart, “Cooperative collision avoidance for nonholonomic robots,” IEEE Transactions on Robotics, vol. 34, no. 2, pp. 404–420, 2018.
- J. van den Berg, M. C. Lin, and D. Manocha, “Reciprocal velocity obstacles for real-time multi-agent navigation,” in Proceedings IEEE International Conference on Robotics & Automation, 2008, pp. 1928–1935.
- J. A. Reeds and L. A. Shepp, “Optimal paths for a car that goes both forwards and backwards,” Pacific journal of mathematics, vol. 71, no. 2, pp. 399–409, 1977.
- D. Dolgov, S. Thrun, M. Montemerlo, and J. Diebel, “Practical search techniques in path planning for autonomous driving,” Ann Arbor, vol. 1001, no. 48105, pp. 18–80, 2008.
- J. P. Van Den Berg and M. H. Overmars, “Prioritized motion planning for multiple robots,” in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005, pp. 430–435.
- H. Tang, R. Houthooft, D. Foote, A. Stooke, O. Xi Chen, Y. Duan, J. Schulman, F. DeTurck, and P. Abbeel, “# exploration: A study of count-based exploration for deep reinforcement learning,” Advances in neural information processing systems, vol. 30, 2017.
- J. Li, A. Tinka, S. Kiesel, J. W. Durham, T. S. Kumar, and S. Koenig, “Lifelong multi-agent path finding in large-scale warehouses,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 13, 2021, pp. 11 272–11 281.
- S. D. Han and J. Yu, “Optimizing space utilization for more effective multi-robot path planning,” in 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 10 709–10 715.
- J. Yu, S. D. Han, W. N. Tang, and D. Rus, “A portable, 3d-printing enabled multi-vehicle platform for robotics research and education,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on. IEEE, 2017, pp. 1475–1480.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.