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

2026-02-15

AI Summary

This work addresses the common practice in robotic warehouse systems of decoupling order scheduling from multi-agent path planning, which overlooks their dynamic interdependence and leads to a mismatch between high-level scheduling decisions and low-level congestion. To bridge this gap, we propose WareRover—a high-fidelity closed-loop simulation platform that enables, for the first time, end-to-end joint modeling of order scheduling and path planning. WareRover incorporates realistic factors such as dynamic order streams, physics-aware motion constraints, heterogeneous robot dynamics, stochastic execution failures, and supports anomaly recovery mechanisms. Experimental results demonstrate that state-of-the-art algorithms exhibit significant performance degradation under this more realistic setting, thereby validating WareRover as a challenging and effective benchmark for evaluating the robustness of next-generation warehouse coordination algorithms.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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/.

Problem

• order scheduling
• multi-agent path finding
• robotic warehouses
• holistic simulation
• coupled optimization

Innovation

• holistic simulation
• order scheduling
• multi-agent path finding
• robotic warehouse
• closed-loop optimization