Improving the Efficiency of Clearing with Multi-agent Teams

  • Authors:

  • Geoffrey Hollinger;Sanjiv Singh;Athanasios Kehagias

  • Affiliations:

  • Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA;Division of Mathematics, Department of Mathematics, Physics, and Computer Sciences, Aristotle University of Thessaloniki, Thessaloniki GR54124, Greece

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2010

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Abstract

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 searching 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 targetâ聙聶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.