Publication

W. Hoenig, S. Kiesel, A. Tinka, J. Durham and N. Ayanian. Persistent and Robust Execution of MAPF Schedules in Warehouses. IEEE Robotics and Automation Letters, 4, (2), 1125-1131, 2019.

Abstract: 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.

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