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Paper TuB05.3

Agmon, Noa (The University of Texas at Austin), Fok, Chien-Liang (The University of Texas at Austin), Elmaliah, Yehuda (Bar-Ilan), Stone, Peter (University of Texas at Austin), Julien, Christine (The University of Texas at Austin), Vishwanath, Sriram (The University of Texas at Austin)

On Coordination in Practical Multi-Robot Patrol

Scheduled for presentation during the Regular Session "Sensor Networks" (TuB05), Tuesday, May 15, 2012, 11:00−11:15, Meeting Room 5 (Ska)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on May 21, 2018

Keywords Path Planning for Multiple Mobile Robots or Agents, Planning, Scheduling and Coordination

Abstract

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 in the real world. In this paper, we evaluate the performance of multi-robot patrol in a practical outdoor robotic system, and evaluate the effect of different coordination schemes on the performance of the robotic team, which is influenced by their communication capabilities and degree of heterogeneity. We specifically focus on frequency-based multi-robot patrol along a cyclic route specified by a set of GPS-waypoints. The multi-robot patrol algorithms evaluated vary in the level of coordination of the robots: no coordination, loose coordination, and strong coordination. In addition, we evaluate versions of these algorithms that distribute state information---either individual state, or state of the entire team (global state). Our experiments show that while strong coordination was theoretically proven to be optimal, in practice uncoordinated patrol performed better in terms of average waypoint visitation frequency. Furthermore, loosely coordinated patrol that shares only individual state outperformed all other coordination schemes in terms of worst-case frequency, and it performed significantly better than a loosely coordinated algorithm based on sharing global-view state. We respond t

 

 

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