arXiv AI By William Zhang, Tzvika Geft, Jingjin Yu, Kostas Bekris

Complete, Scalable, and Robust Prioritized Planning for Multi-Robot Ordered Storage and Retrieval at Maximum Capacity

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arXiv:2608. 07734v1 Announce Type: cross Abstract: Automated warehouses face a fundamental trade-off between maximizing storage density and achieving high retrieval throughput.

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arXiv AI
Aug 26

Pivot-and-Station Multi-Agent Path Finding: Solvability, Complexity, and Algorithms

The paper introduces Pivot-and-Station Multi-Agent Path Finding (PS‑MAPF), a variant of MAPF where a subset of agents must visit interchangeable pivots before all agents occupy anonymous stations. It provides a full solvability characterization: every instance on a 2‑edge‑connected graph is solvable, and for arbitrary connected graphs a structural effective‑distance measure relative to unoccupied vertices gives a necessary and sufficient condition. The authors prove that minimizing station‑makespan or station‑flowtime is NP‑hard even with a single pivot, and present three algorithms—a complete baseline, a SAT‑based optimal solver, and Pivot‑Prioritized Planning (PPP), which solves 74‑89% of benchmark instances with significantly lower makespan and flowtime than the baseline.

By Andrea Di Nezza, Mihir Patel, Fabio Fagnani, Sara Bernardini
arXiv AI
Aug 7

Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations

arXiv:2608. 05588v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones.

By He Jiang, Jingtian Yan, Yulun Zhang, Yimin Tang, Tanishq Duhan, Rishi Veerapaneni, Guillaume Sartoretti, Jiaoyang Li