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

2026-08-07

AI Summary

This study addresses the challenge of deadlock-prone and computationally intensive multi-robot coordination in high-density, aisle-free warehouse systems, where maximizing storage capacity while ensuring efficient retrieval is difficult. Focusing on ordered retrieval under maximum storage capacity, this work proposes an online priority-based multi-robot path planning method that leverages the non-relocation layout property. The approach uniquely integrates geometric feasibility with execution efficiency, designing a complete and scalable priority algorithm based on storage layout invariants to provably avoid deadlocks. It robustly handles uncertainty in departure sequences with negligible computational overhead. Experiments demonstrate that task completion time decreases nearly linearly with the number of robots, supporting up to as many robots as the grid width, while the robust variant incurs no significant performance loss compared to non-robust baselines.

Abstract

Automated warehouses face a fundamental trade-off between maximizing storage density and achieving high retrieval throughput. While puzzle-based storage (PBS) architectures increase capacity by eliminating aisles, coordinating multiple robots in these high-density spaces is computationally challenging due to the potential for deadlocks. This paper introduces a novel multi-robot formulation for the ``ordered storage and retrieval problem at maximum capacity''. The focus is on rectangular grids accessible from a single boundary, where loads need to be first stored up to full capacity and then efficiently retrieved, given a planned departure sequence. This work bridges the gap between geometric feasibility and execution efficiency by leveraging the properties of relocation-free arrangements. These properties guide an online, prioritized multi-agent path-finding algorithm, which is the main contribution of this work. Unlike general centralized planners, the approach exploits the specific invariants of the storage arrangement to guarantee completeness and prevent deadlocks, enabling scalability. Experiments demonstrate that the method achieves near-linear improvement in makespan with respect to the number of robots, up to $m = C$, where $C$ is the grid width. Crucially, the algorithmic overhead of supporting robustness is negligible; the system handles uncertainty in departure sequences using robust storage arrangements with no significant penalty in execution speed compared to the non-robust baseline.

Problem

• multi-robot coordination
• ordered storage and retrieval
• maximum capacity
• deadlock avoidance
• automated warehouses

Innovation

• prioritized planning
• multi-robot path finding
• relocation-free arrangement
• deadlock-free coordination
• robust storage