Multi-robot patrol is a fundamental application of multi-robot systems. While much theoretical work exists providing an understanding of the optimal patrol strategy for teams of coordinated homogeneous robots, little work exists on building and evaluating the performance of such systems for real. In this paper, we evaluate the performance of multi-robot patrol in a practical outdoor distributed robotic system, and evaluate the effect of different coordination schemes on the performance of the robotic team. The multi-robot patrol algorithms evaluated vary in the level of robot coordination: no coordination, loose coordination, and tight coordination. In addition, we evaluate versions of these algorithms that distribute state information—either individual state, or entire team state (global-view state). Our experiments show that while tight coordination is theoretically optimal, it is not practical in practice. Instead, uncoordinated patrol performs best in terms of average waypoint visitation frequency, though loosely coordinated patrol that shares only individual state performed best in terms of worst-case frequency. Both are significantly better than a loosely coordinated algorithm based on sharing global-view state. We respond to this discrepancy between theory and practice, caused primarily by robot heterogeneity, by extending the theory to account for such heterogeneity, and find that the new theory accounts for the empirical results.

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In {IEEE} International Conference on Robotics and Automation (ICRA), May 2012.

@inproceedings{ICRA12-agmon, title={On Coordination in Practical Multi-Robot Patrol}, author={Noa Agmon and Chien-Liang Fok and Yehuda Emaliah and Peter Stone and Christine Julien and Sriram Vishwanath}, booktitle={{IEEE} International Conference on Robotics and Automation (ICRA)}, month={May}, url="http://www.cs.utexas.edu/users/ai-lab?ICRA12-agmon", year={2012} }