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CTS-PIBT: An Efficient Method for Multi-Agent Collaborative Task Sequencing and Path Finding
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DOI：10.1109/LRA.2025.3604726.png)
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Abstract
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The Collaborative Task Sequencing and Multi-Agent Path Finding (CTS-MAPF) problem involves planning collision-free paths for multiple agents while determining the sequence of intermediate tasks. This problem is particularly challenging due to its combinatorial complexity, as it combines both task sequencing and pathfinding. This letter introduces CTS-PIBT, a novel and efficient algorithm designed to address the CTS-MAPF problem. CTS-PIBT adopts a hierarchical framework with three key components: task sequencing, solution finding following the sequence, and a low-level search using an extended version of PIBT. This framework effectively leverages the advantages of the configuration-based approach, enabling the rapid generation of feasible solutions within a short period. To further enhance performance, we incorporate an anytime refinement mechanism and a quick task sequencing technique (called greedy insertion with 2-opt) to improve solution quality and solving efficiency. Extensive simulations demonstrate that CTS-PIBT significantly outperforms existing methods in success rate and runtime, particularly in large-scale and complex scenarios. Furthermore, physical robot experiments validate its practical applicability in real-world environments.
Keywords:
Multi-agent path finding
multi-agent collaborative task sequencing
priority inheritance with backtracking
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Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
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