Toward efficient trajectory planning: the path-velocity decomposition
International Journal of Robotics Research
Robot Motion Planning
A new approach to cooperative pathfinding
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Exploiting subgraph structure in multi-robot path planning
Journal of Artificial Intelligence Research
Scalable Multi-Agent Pathfinding on Grid Maps with Tractability and Completeness Guarantees
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
An enhanced formation of multi-robot based on A* algorithm for data relay transmission
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Massively multi-agent pathfinding made tractable, efficient, and with completeness guarantees
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multi-robot path planning with the spatio-temporal a* algorithm and its variants
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
MAPP: a scalable multi-agent path planning algorithm with tractability and completeness guarantees
Journal of Artificial Intelligence Research
Push and swap: fast cooperative path-finding with completeness guarantees
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Tractable massively multi-agent pathfinding with solution quality and completeness guarantees
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
The increasing cost tree search for optimal multi-agent pathfinding
Artificial Intelligence
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Multi-agent path planning on grid maps is a challenging problem and has numerous real-life applications. Running a centralized, systematic search such as A* is complete and cost-optimal but scales up poorly in practice, since both the search space and the branching factor grow exponentially in the number of mobile units. Decentralized approaches, which decompose a problem into several subproblems, can be faster and can work for larger problems. However, existing decentralized methods offer no guarantees with respect to completeness, running time, and solution quality. To address such limitations, we introduce MAPP, a tractable algorithm for multi-agent path planning on grid maps. We show that MAPP has low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. As it runs in low-polynomial time, MAPP is incomplete in the general case. We identify a class of problems for which our algorithm is complete. We believe that this is the first study that formalises restrictions to obtain a tractable class of multi-agent path planning problems.