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

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.

Figures (7)

• Figure 1: Illustration of warehouse robots rearranging shelves nypost2025robots.
• Figure 2: CREST running example with two agents ($a_1$, $a_2$) and four shelves ($s_1$–$s_4$). (a) shows the initial $\mathcal{T}$ and $D$ at $t=0$. Each shelf trajectory in $\mathcal{T}$ is an ordered waypoint sequence, and $D$ is a precedence graph defined over these waypoints; solid arrows denote Type-1 arcs and dashed arrows denote Type-2 arcs. (b) shows the simplified $\mathcal{T}$ and $D$ at $t = 0$, obtained by merging consecutive waypoints for each shelf. (c) shows the final execution produced by CREST from $t = 0$ to $t = 7$.
• Figure 3: STR example with one agent ($a_1$) and three shelves ($s_1$, $s_2$, $s_3$). With $a_1$ assigned to $s_3$, the baseline detours to avoid $s_1$ and $s_2$ (left). STR replans the remaining path of $s_3$ to shorten the unexecuted segment and avoid being blocked at $(3,1)$ (right).
• Figure 4: DS and GTR example with one agent ($a_1$) and two shelves ($s_1$ and $s_2$). Agent $a_1$ carries $s_2$ first. When $s_2$ is constrained by $s_1$ at location $(2,3)$, DS reverses the corresponding Type-2 dependencies to allow $s_2$ to proceed first, whereas GTR replans the unexecuted portion of $s_1$’s path to free the route for $s_2$.
• Figure 5: Distributions of shelf pickup (top) and delivery (bottom) cells (brown) and agent initial cells (blue) across three layouts.