The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

For some tasks, the use of more than one robot may improve the speed, reliability, or flexibility of completion, but many other tasks can be completed only by multiple robots. This paper investigates controller design using multi-objective genetic programming for a multi-robot system to solve a highly constrained problem, where multiple unmanned aerial vehicles (UAVs) must monitor targets spread sparsely throughout a large area. UAVs have a small communication range, sensor information is limited and noisy, monitoring a target takes an indefinite amount of time, and evolved controllers must continue to perform well even as the number of UAVs and targets changes. An evolved task selection controller dynamically chooses a target for the UAV based on sensor information and communication. Controllers evolved using several communication schemes were compared on problem scenarios of varying size, and the results suggest that this approach can evolve effective controllers if communication is limited to the nearest other UAV.

Notes

Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems

Associated Lab(s) / Group(s): Intelligent Coordination and Logistics Laboratory

Number of pages: 16

Text Reference

Gregory Barlow, Choong Oh, and Stephen Smith , "Evolving Cooperative Control on Sparsely Distributed Tasks for UAV Teams Without Global Communication," tech. report CMU-RI-TR-07-24, Robotics Institute, Carnegie Mellon University, July, 2007

BibTeX Reference

@techreport{Barlow_2007_5803,
   author = "Gregory Barlow and Choong Oh and Stephen {Smith }",
   title = "Evolving Cooperative Control on Sparsely Distributed Tasks for UAV Teams Without Global Communication",
   booktitle = "",
   institution = "Robotics Institute",
   month = "July",
   year = "2007",
   number= "CMU-RI-TR-07-24",
   address= "Pittsburgh, PA",
}

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