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Learning Opportunity Costs in Multi-Robot Market Based Planners

J. Schneider,David Apfelbaum,J. Bagnell,R. Simmons

Published 2005 in Proceedings of the 2005 IEEE International Conference on Robotics and Automation

ABSTRACT

Direct human control of multi-robot systems is limited by the cognitive ability of humans to coordinate numerous interacting components. In remote environments, such as those encountered during planetary or ocean exploration, a further limit is imposed by communication bandwidth and delay. Market based planning can give humans a higher-level interface to multi-robot systems in these scenarios. Operators provide high level tasks and attach a reward to the achievement of each task. The robots then trade these tasks through a market based mechanism. The challenge for the system designer is to create bidding algorithms for the robots that yield high overall system performance. Opportunity cost provides a nice basis for such bidding algorithms since it encapsulates all the costs and benefits we are interested in. Unfortunately, computing it can be difficult. We propose a method of learning opportunity costs in market based planners. We provide analytic results in simplified scenarios and empirical results on our FIRE simulator, which focuses on exploration of Mars by multiple, heterogeneous rovers.

PUBLICATION RECORD

  • Publication year

    2005

  • Venue

    Proceedings of the 2005 IEEE International Conference on Robotics and Automation

  • Publication date

    2005-04-18

  • Fields of study

    Engineering, Computer Science, Economics

  • Identifiers
    DOI 10.1109/ROBOT.2005.1570271
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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