CREST通过动态释放约束，让多机器人货架重排更高效连续。
CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement
- 执行中主动释放轨迹约束，避免机器人空等和频繁换货。
- 相比旧方法，行程减少40.5%，任务周期缩短33.3%，换货次数降44.4%。
- 特别适合高搬运开销的仓库场景，提升系统整体效率。
双层多智能体取送货（DD-MAPD）建模自动化仓库中的多机器人货架重排问题。MAPF-DECOMP 是一种新框架，先用地图路径规划（MAPF）求解无碰撞的货架轨迹，再分配机器人执行。虽高效，但强制依赖轨迹，常导致机器人闲置和不必要的货架切换，影响执行质量。本文提出 CREST，一种新的执行框架，在执行过程中主动释放轨迹约束，实现更连贯的货架搬运。在多种仓库布局上的实验表明，CREST 持续优于 MAPF-DECOMP，使与机器人行程、任务周期和货架切换相关的指标分别降低最多 40.5%、33.3% 和 44.4%，且在搬运开销较高时优势更显著。结果凸显了执行感知的约束释放对可扩展仓库重排的重要性。代码与数据见 https://github.com/ChristinaTan0704/CREST。
原文摘要 · Abstract (English)
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.
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