提出一体化仿真平台，解决机器人仓库中订单调度与路径规划的耦合难题。
It Takes Two to Tango: A Holistic Simulator for Joint Order Scheduling and Multi-Agent Path Finding in Robotic Warehouses
- 构建闭环优化接口，统一调度与路径规划，实现动态协同。
- 引入真实运动约束和故障恢复机制，使测试更贴近实际场景。
- 揭示现有算法在复杂耦合条件下的失效问题，适合研发鲁棒系统者使用。
当前机器人移动分拣系统通常将订单调度（OS）与多智能体路径规划（MAPF）视为独立问题。我们指出这种解耦是根本瓶颈，掩盖了高层调度与底层拥堵之间的关键依赖。现有仿真器未能弥合此差距，常忽略异构运动特性与随机执行失败。为此，我们提出 WareRover，一个统一的全栈仿真平台，通过闭环优化接口紧密耦合 OS 与 MAPF。不同于传统基准，WareRover 在单一评估循环中整合动态订单流、物理感知运动约束及非理想状态恢复机制。实验表明，当前最优算法在此类真实耦合约束下表现显著下降，证明 WareRover 提供了必要且具挑战性的测试环境，可用于下一代仓库协同系统的鲁棒性验证。项目与视频见 https://hhh-x.github.io/WareRover/。
原文摘要 · Abstract (English)
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/.
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