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Local Solution Repair for Multi-Agent Pickup and Delivery — arXiv:2611.02090

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02090
License (as archived): CC0-1.0
Archived: 2026-11-03T10:19:45.141Z
SHA-256 of the archived text: 7aa3d8785e8af8e20b573441a3632374895b83c7f42ade46040f17b3f0be6f61
Stable URL of this page: https://metraipolis.com/documento/dc_cd8da161aa8f

How to cite

Local Solution Repair for Multi-Agent Pickup and Delivery — arXiv:2611.02090 — arXiv (en), https://arxiv.org/abs/2611.02090 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd8da161aa8f (Metraipolis archive dc_cd8da161aa8f, sha256 7aa3d8785e8af8e20b573441a3632374895b83c7f42ade46040f17b3f0be6f61)

BibTeX

@misc{dccd8da161aa8f,
  title = {Local Solution Repair for Multi-Agent Pickup and Delivery — arXiv:2611.02090},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02090},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd8da161aa8f; sha256 7aa3d8785e8af8e20b573441a3632374895b83c7f42ade46040f17b3f0be6f61},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02090. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-robot systems in automated warehouses must efficiently attend to a constant stream of pickup-and-delivery tasks while avoiding collisions. Existing Multi-Agent Pickup-and-Delivery (MAPD) solutions generate plans that are globally feasible but often locally inefficient. In large-scale, long-horizon deployments, these inefficiencies, such as robots backtracking from target goal locations and idling in congested areas, accumulate over time and degrade performance. This paper contributes Local Solution Repair (LSR), a goal replanning approach that detects suboptimal behaviors at run-time and locally improves plans to reduce conflict between agents. We demonstrate the utility of our approach by repairing solutions generated by state-of-the-art MAPD baselines, M2M and LNS-PBS. Our results in long-horizon warehouse simulations over three environments show upwards of 5.9% improvement in task throughput over current state-of-the-art methods by reducing the number of suboptimal instances by ~36k or by 14.6% with minor additional computation time.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2611.02090 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:19:34.836Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).