/Improving the Efficiency of Clearing with Multi-agent Teams

Improving the Efficiency of Clearing with Multi-agent Teams

Geoffrey Hollinger, Sanjiv Singh and Athanasios Kehagias
Journal Article, Carnegie Mellon University, International Journal of Robotics Research, Vol. 29, No. 8, pp. 1088-1105, July, 2010

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We present an anytime algorithm for coordinating multiple autonomous searchers to find a potentially adversarial target on a graphical representation of a physical environment. This problem is closely related to the mathematical problem of earching for an adversary on a graph. Prior methods in the literature treat multi-agent search as either a worst-case problem (i.e. clear an environment of an adversarial evader with potentially infinite speed), or an average-case problem (i.e. minimize average capture time given a model of the target’s motion). Both of these problems have been shown to be NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We propose treating search as a resource allocation problem, which leads to a scalable anytime algorithm for generating schedules that clear the environment of a worst-case adversarial target and have good average-case performance considering a non-adversarial motion model. Our algorithm yields theoretically bounded average-case performance and allows for online and decentralized operation, making it applicable to real-world search tasks. We validate our proposed algorithm through a large number of experiments in simulation and with a team of robot and human searchers in an office building.

BibTeX Reference
author = {Geoffrey Hollinger and Sanjiv Singh and Athanasios Kehagias},
title = {Improving the Efficiency of Clearing with Multi-agent Teams},
journal = {International Journal of Robotics Research},
year = {2010},
month = {July},
volume = {29},
number = {8},
pages = {1088-1105},
keywords = {multi-robot coordination, autonomous search, pursuit/evasion, decentralized planning},