CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement

TL;DR

CREST is a novel execution framework for multi-robot warehouse shelf rearrangement that improves efficiency by proactively releasing trajectory constraints, outperforming previous methods across multiple metrics.

Contribution

It introduces CREST, a new execution approach that enhances multi-robot warehouse operations by enabling more continuous shelf carrying and reducing unnecessary movements.

Findings

CREST reduces agent travel by up to 40.5%.

CREST decreases makespan by up to 33.3%.

CREST cuts shelf switching by up to 44.4%.

Abstract

Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD) models the multi-robot shelf rearrangement problem in automated warehouses. MAPF-DECOMP is a recent framework that first computes collision-free shelf trajectories with a MAPF solver and then assigns agents to execute them. While efficient, it enforces strict trajectory dependencies, often leading to poor execution quality due to idle agents and unnecessary shelf switching. We introduce CREST, a new execution framework that achieves more continuous shelf carrying by proactively releasing trajectory constraints during execution. Experiments on diverse warehouse layouts show that CREST consistently outperforms MAPF-DECOMP, reducing metrics related to agent travel, makespan, and shelf switching by up to 40.5\%, 33.3\%, and 44.4\%, respectively, with even greater benefits under lift/place overhead. These results underscore the importance of execution-aware constraint release for scalable warehouse rearrangement. Code and data are available at https://github.com/ChristinaTan0704/CREST.