日本フィジカルAI新聞

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週刊ニュースレター購読
arXiv:2509.01022

Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents

Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents

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著者: Bo Fu, Zhe Chen, Rahul Chandan, Alex Barbosa, Michael Caldara, Joey Durham, Federico Pecora

分類: cs.AI, cs.MA, cs.RO

原文アブストラクト

We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a goal state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveraging joint configuration space search, classical planning, multi-agent pathfinding, and expert heuristics. We evaluate the five approaches empirically for plan quality and scalability. Despite the exponential relation between search space size and block number, our methods demonstrate efficiency in creating rearrangement plans for deeply buried blocks in up to 80x80 grids.