Persistent and Robust Execution of MAPF Schedules in Warehouses
Persistent and Robust Execution of MAPF Schedules in Warehouses
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DOI：
10.1109/lra.2019.2894217
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发表时间：
2019-01
影响因子：
5.2
通讯作者：
W. Hönig;Scott Kiesel;Andrew Tinka;Joseph W. Durham;Nora Ayanian
中科院分区：
文献类型：
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作者：
W. Hönig;Scott Kiesel;Andrew Tinka;Joseph W. Durham;Nora Ayanian
多代理路径查找（MAPF）是人工智能中一个充分研究的问题，在使用简化的代理假设时，可以在实践中快速解决。但是，现实世界中的应用程序（例如仓库自动化）需要物理机器人在不相撞的情况下长期运行。我们提出了一个可以使用现有的单杆MAPF计划者的执行框架，并确保在存在未知或随时间变化的高阶动态限制，不可预见的机器人减速和不可预测的障碍物外观的情况下确保强大的执行。我们的框架自然可以使重新计划和执行的重叠以进行持续操作，并且需要在机器人与集中规划者之间进行沟通。我们在仓库模拟和使用差速器机器人的混合现实实验中演示了我们的方法。我们认为，我们的解决方案缩小了人工智能界最近的研究与现实世界应用之间的差距。
Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence that can be solved quickly in practice when using simplified agent assumptions. However, real-world applications, such as warehouse automation, require physical robots to function over long time horizons without collisions. We present an execution framework that can use existing single-shot MAPF planners and ensures robust execution in the presence of unknown or time-varying higher-order dynamic limits, unforeseen robot slow-downs, and unpredictable obstacle appearances. Our framework also naturally enables the overlap of re-planning and execution for persistent operation and requires little communication between robots and the centralized planner. We demonstrate our approach in warehouse simulations and in a mixed reality experiment using differential drive robots. We believe that our solution closes the gap between recent research in the artificial intelligence community and real-world applications.