Persistent and Robust Execution of MAPF Schedules in Warehouses
Wolfgang Hönig, Scott Kiesel, Andrew Tinka, Joseph W. Durham, Nora Ayanian
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.
BibTeX
@inproceedings{ral2019_persistentandrob,
title = {Persistent and Robust Execution of MAPF Schedules in Warehouses},
author = {Wolfgang Hönig and Scott Kiesel and Andrew Tinka and Joseph W. Durham and Nora Ayanian},
booktitle = {RA-L 2019},
year = {2019}
}