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Paper TuB05.2

Bays, Matthew (Virginia Tech), Shende, Apoorva (Virginia Tech), Stilwell, Daniel (Virginia Tech)

An Approach to Multi-Agent Area Protection Using Bayes Risk

Scheduled for presentation during the Regular Session "Sensor Networks" (TuB05), Tuesday, May 15, 2012, 10:45−11:00, 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 December 11, 2017

Keywords Distributed Robot Systems, Motion and Path Planning, Agent-Based Systems

Abstract

We introduce a novel approach to controlling the motion of a team of agents so that they jointly minimize a cost function utilizing Bayes risk. We use a particle-based approach and approximations that allow us to express the optimization problem as a mixed-integer linear program. We illustrate this approach with an area protection problem in which a team of mobile agents must intercept mobile targets before the targets enter a specified area. Bayes risk is a useful measure of performance for applications where agents must perform a classification task. By minimizing Bayes risk, agents are able to explicitly account for the cost of incorrect classification. In our application, a team of mobile agents must classify potential mobile targets as threat or safe based on the likelihood the targets will enter the specified area. The agents must also maneuver to intercept targets that are classified as threat.

 

 

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