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/.

翻译：在机器人移动拣选系统中，当前主流范式通常将订单调度与多智能体路径规划视为相互独立的子问题。我们认为这种解耦是根本性的瓶颈，掩盖了高层调度与底层拥堵之间的关键依赖关系。现有仿真器未能弥合这一鸿沟，往往忽略了异构运动学特性和随机执行故障。我们提出WareRover——一个通过统一闭环优化接口实现订单调度与多智能体路径规划紧密耦合的集成仿真平台。与标准基准测试不同，WareRover将动态订单流、物理感知运动约束和非标称恢复机制整合到单一评估循环中。实验表明，现有先进算法在这些现实耦合约束下常出现失效，证明WareRover为构建鲁棒的新一代仓库协调系统提供了必要且具有挑战性的测试平台。项目详情与演示视频请访问：https://hhh-x.github.io/WareRover/。