Safe Interval RRT* for Scalable Multi-Robot Path Planning in Continuous Space (2404.01752v3)
Abstract: In this paper, we consider the problem of Multi-Robot Path Planning (MRPP) in continuous space. The difficulty of the problem arises from the extremely large search space caused by the combinatorial nature of the problem and the continuous state space. We propose a two-level approach where the low level is a sampling-based planner Safe Interval RRT* (SI-RRT*) that finds a collision-free trajectory for individual robots. The high level can use any method that can resolve inter-robot conflicts where we employ two representative methods that are Prioritized Planning (SI-CPP) and Conflict Based Search (SI-CCBS). Experimental results show that SI-RRT* can quickly find a high-quality solution with a few samples. SI-CPP exhibits improved scalability while SI-CCBS produces higher-quality solutions compared to the state-of-the-art planners for continuous space.
- 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,” in Proceedings of the International Symposium on Combinatorial Search, vol. 10, no. 1, 2019, pp. 151–158.
- J. Yu and S. LaValle, “Structure and intractability of optimal multi-robot path planning on graphs,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 27, no. 1, 2013, pp. 1443–1449.
- G. Wagner and H. Choset, “Subdimensional expansion for multirobot path planning,” Artificial intelligence, vol. 219, pp. 1–24, 2015.
- T. Standley, “Finding optimal solutions to cooperative pathfinding problems,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 24, no. 1, 2010, pp. 173–178.
- M. Goldenberg, A. Felner, R. Stern, G. Sharon, N. Sturtevant, R. C. Holte, and J. Schaeffer, “Enhanced partial expansion a,” Journal of Artificial Intelligence Research, vol. 50, pp. 141–187, 2014.
- P. Velagapudi, K. Sycara, and P. Scerri, “Decentralized prioritized planning in large multirobot teams,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2010, pp. 4603–4609.
- D. Silver, “Cooperative pathfinding,” in Proceedings of the aaai conference on artificial intelligence and interactive digital entertainment, vol. 1, no. 1, 2005, pp. 117–122.
- 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.
- M. Barer, G. Sharon, R. Stern, and A. Felner, “Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem,” in Proceedings of the International Symposium on Combinatorial Search, vol. 5, no. 1, 2014, pp. 19–27.
- J. Li, W. Ruml, and S. Koenig, “EECBS: A bounded-suboptimal search for multi-agent path finding,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 14, 2021, pp. 12 353–12 362.
- M. Phillips and M. Likhachev, “Sipp: Safe interval path planning for dynamic environments,” in Proceedings of IEEE International Conference on Robotics and Automation, 2011, pp. 5628–5635.
- K. Okumura and X. Défago, “Quick multi-robot motion planning by combining sampling and search,” in Proceedings of International Joint Conferences on Artificial Intelligence, 2023.
- F. Grothe, V. N. Hartmann, A. Orthey, and M. Toussaint, “ST-RRT*{}^{*}start_FLOATSUPERSCRIPT * end_FLOATSUPERSCRIPT: Asymptotically-optimal bidirectional motion planning through space-time,” in Proceedings of International Conference on Robotics and Automation. IEEE, 2022, pp. 3314–3320.
- C. Yu, Q. Li, S. Gao, and A. Prorok, “Accelerating multi-agent planning using graph transformers with bounded suboptimality,” in Proceedings of International Conference on Robotics and Automation, 2023.
- A. Andreychuk, K. Yakovlev, D. Atzmon, and R. Sternr, “Multi-agent pathfinding with continuous time,” in Proceedings of International Joint Conference on Artificial Intelligence, 2019, pp. 39–45.
- A. Orthey, S. Akbar, and M. Toussaint, “Multilevel motion planning: A fiber bundle formulation,” arXiv preprint arXiv:2007.09435, 2020.
- J. Li, Z. Chen, D. Harabor, P. J. Stuckey, and S. Koenig, “MAPF-LNS2: Fast repairing for multi-agent path finding via large neighborhood search,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 9, 2022, pp. 10 256–10 265.
- I. Solis, J. Motes, R. Sandström, and N. M. Amato, “Representation-optimal multi-robot motion planning using conflict-based search,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4608–4615, 2021.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.