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arXiv · 2109.04264

Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution

Keisuke Okumura·Xavier Défago
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Abstract

Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of Multi-Agent Path Finding (MAPF), is crucial for high-level coordination in multi-agent systems, e.g., pattern formation by robot swarms. This paper studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large instances by centralized approaches with small computation time, and (2) online scenario: executing unlabeled-MAPF despite timing uncertainties of real robots. For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm, which takes an arbitrary initial target assignment then repeats one-timestep path planning with target swapping. TSWAP can adapt to both offline and online scenarios. We empirically demonstrate that Offline TSWAP is highly scalable; providing near-optimal solutions while reducing runtime by orders of magnitude compared to existing approaches. In addition, we present the benefits of Online TSWAP, such as delay tolerance, through real-robot demos.

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Keisuke Okumura, Xavier Défago. 2021-09-09. Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution. https://arxiv.org/abs/2109.04264

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