The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Finding Optimal Solutions to Cooperative Pathfinding Problems
Open accessAbstract
In cooperative pathfinding problems, non-interfering paths that bring each agent from its current state to its goal state must be planned for multiple agents. We present the first practical, admissible, and complete algorithm for solving problems of this kind. First, we propose a technique called operator decomposition, which can be used to reduce the branching factors of many search algorithms, including algorithms for cooperative pathfinding. We then show how a type of independence common in instances of cooperative pathfinding problems can be exploited. Next, we take the idea of exploiting independent subproblems further by adding improvements that allow the algorithm to recognize many more cases of such independence. Finally, we show empirically that these techniques drastically improve the performance of the standard admissible algorithm for the cooperative pathfinding problem, and that their combination results in a complete algorithm capable of optimally solving relatively large problems in milliseconds.
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Cites
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738
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Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism
Dresner, K., & Stone, P. (2004) · doi:10.1109/aamas.2004.190 · Cited by 461
Coordinated path planning for multiple robots
Švestka, P., & Overmars, M. H. (1998) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(97)00033-x · Cited by 270
Tractable multi-agent path planning on grid maps
Wang, K.-H. C., & Botea, A. (2009) · Cited by 65
Cited by
Conflict-based search for optimal multi-agent pathfinding
Sharon, G., Stern, R., Felner, A., & Sturtevant, N. R. (2015) · Artificial Intelligence · doi:10.1016/j.artint.2014.11.006 · Cited by 1,242
PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning
Sartoretti, G., Kerr, J., Shi, Y., Wagner, G., Kumar, T. K. S., Koenig, S., & Choset, H. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2903261 · Cited by 417
Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics
Yu, J., & LaValle, S. M. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2593448 · Cited by 331
Fujitani, Y., Yamauchi, T., Miyashita, Y., & Sugawara, T. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.212 · Cited by 9
Metadata
- Authors
- Standley, T.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2010-07-03
- Volume
- 24
- Issue
- 1
- Pages
- 173-178
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W1551562380
- Cited by
- 358
- References
- 6
- FWCI
- 4.40
- Type
- conference paper
- Language
- en
- Open access
- diamond
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