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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.01022 |
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| _version_ | 1866911361694433280 |
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| author | Fu, Bo Chen, Zhe Chandan, Rahul Barbosa, Alex Caldara, Michael Durham, Joey Pecora, Federico |
| author_facet | Fu, Bo Chen, Zhe Chandan, Rahul Barbosa, Alex Caldara, Michael Durham, Joey Pecora, Federico |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01022 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents Fu, Bo Chen, Zhe Chandan, Rahul Barbosa, Alex Caldara, Michael Durham, Joey Pecora, Federico Artificial Intelligence Multiagent Systems Robotics 93A16 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. |
| title | Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents |
| topic | Artificial Intelligence Multiagent Systems Robotics 93A16 |
| url | https://arxiv.org/abs/2509.01022 |