Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs

Curt Bererton
doctoral dissertation, tech. report CMU-RI-TR-04-65, Robotics Institute, Carnegie Mellon University, August, 2004


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Abstract
Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs) are preferred methods representing complex uncertain dynamic systems and determining an optimal control policy to manipulate the system in the desired manner. Until recently, controlling a system composed of multiple agents using the MDP methodology was impossible due to an exponential increase in the size of the MDP problem representation. In this thesis, a novel method for solving large multi-agent MDP systems is presented which avoids this exponential size increase while still providing optimal policies for a large class of useful problems. This thesis provides the following main contributions:
    A game of robot paintball: Using the above techniques we solve a game of multi-robot paintball. This game is played with real robots at high speeds.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Number of pages: 183

Text Reference
Curt Bererton, "Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs," doctoral dissertation, tech. report CMU-RI-TR-04-65, Robotics Institute, Carnegie Mellon University, August, 2004

BibTeX Reference
@phdthesis{Bererton_2004_4871,
   author = "Curt Bererton",
   title = "Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs",
   booktitle = "",
   school = "Robotics Institute, Carnegie Mellon University",
   month = "August",
   year = "2004",
   number= "CMU-RI-TR-04-65",
   address= "Pittsburgh, PA",
}