It Takes Two to Tango: A Holistic Simulator for Joint Order Scheduling and Multi-Agent Path Finding in Robotic Warehouses

TL;DR

WareRover is a comprehensive simulation platform that tightly integrates order scheduling and multi-agent pathfinding in robotic warehouses, revealing limitations of current algorithms under realistic, coupled constraints.

Contribution

This paper introduces WareRover, a holistic simulator that unifies order scheduling and pathfinding with realistic constraints, bridging a critical gap in warehouse robotics simulation.

Findings

State-of-the-art algorithms often fail under coupled, realistic constraints.

WareRover enables evaluation of robust coordination strategies in complex scenarios.

The platform incorporates dynamic orders, physics, and recovery mechanisms.

Abstract

The prevailing paradigm in Robotic Mobile Fulfillment Systems (RMFS) typically treats order scheduling and multi-agent pathfinding as isolated sub-problems. We argue that this decoupling is a fundamental bottleneck, masking the critical dependencies between high-level dispatching and low-level congestion. Existing simulators fail to bridge this gap, often abstracting away heterogeneous kinematics and stochastic execution failures. We propose WareRover, a holistic simulation platform that enforces a tight coupling between OS and MAPF via a unified, closed-loop optimization interface. Unlike standard benchmarks, WareRover integrates dynamic order streams, physics-aware motion constraints, and non-nominal recovery mechanisms into a single evaluation loop. Experiments reveal that SOTA algorithms often falter under these realistic coupled constraints, demonstrating that WareRover provides a necessary and challenging testbed for robust, next-generation warehouse coordination. The project and video is available at https://hhh-x.github.io/WareRover/.