CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement
Summary: arXiv:2603.28803v1 Announce Type: cross
Abstract
The Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD) problem focuses on the multi-robot shelf rearrangement tasks within automated warehouses.
While the MAPF-DECOMP framework is an innovative approach that computes collision-free shelf trajectories utilizing a MAPF solver, it has notable drawbacks.
The framework enforces strict trajectory dependencies, which can lead to inefficiencies such as idle agents and unnecessary shelf switching during execution.
To address these challenges, we introduce CREST, a new execution framework that enhances warehouse operations by proactively releasing trajectory constraints during execution.
Our extensive experiments across various warehouse layouts demonstrate that CREST consistently outperforms MAPF-DECOMP, achieving substantial reductions in metrics related to agent travel, makespan, and shelf switching.
Specifically, we observe improvements of up to 40.5%, 33.3%, and 44.4%, respectively, particularly under conditions of lift/place overhead.
These findings highlight the significance of execution-aware constraint release as a critical factor for scalable warehouse rearrangement.
For further exploration, our code and data are accessible at https://github.com/ChristinaTan0704/CREST.
Introduction
The automation of warehouse logistics has become increasingly critical in modern supply chain management.
With the rise of e-commerce and the growing demand for efficiency, multi-robot systems have emerged as a promising solution for optimizing warehouse operations.
However, the problem of shelf rearrangement remains a significant challenge, particularly when multiple robots are tasked with the same objective.
Overview of MAPF-DECOMP
The MAPF-DECOMP framework represents a significant advancement in solving the DD-MAPD problem.
By utilizing a Multi-Agent Pathfinding (MAPF) solver, the framework computes optimal trajectories that avoid collisions among agents.
However, the rigid dependencies imposed by this method can lead to suboptimal performance, particularly in dynamic environments.
Introducing CREST
CREST offers a novel approach to execution by allowing for the dynamic release of trajectory constraints.
This flexibility enables agents to adapt their movements in real time, reducing idle times and unnecessary transitions between shelves.
The following points summarize the key advantages of CREST:
- Enhanced continuous shelf carrying, allowing for better utilization of robot capabilities.
- Significant reductions in overall travel distances for agents, leading to increased efficiency.
- Improved overall makespan, resulting in faster completion of shelf rearrangement tasks.
- Minimized shelf switching, which reduces wear and tear on equipment and improves operational flow.
Experimental Results
Our experimental results reveal a clear advantage of CREST over MAPF-DECOMP.
In diverse warehouse layouts, we observed that the new framework reduced agent travel distances by up to 40.5%,
while also decreasing the overall makespan by 33.3%.
Additionally, shelf switching was minimized by 44.4%, demonstrating CREST’s effectiveness in practical applications.
Conclusion
The introduction of CREST marks a significant advancement in the realm of multi-robot warehouse operations.
By prioritizing execution-aware constraint release, CREST offers a scalable solution for efficient warehouse shelf rearrangement.
As the demand for automated logistics continues to grow, frameworks like CREST are essential for optimizing performance and enhancing operational efficiency.