UAV Cooperative Control with Stochastic Risk Models
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How_UAV cooperative.pdf
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582.34 KB
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Author(s)
Geramifard, Alborz
Redding, Joshua
Roy, Nicholas
How, Jonathan P.
Date Issued
June 2011
Journal
Proceedings of the 2011 American Control Conference
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Geramifard, Alborz et al. "UAV Cooperative Control with Stochastic Risk Models." IEEE American Control Conference, 2011.
Version
Author's final manuscript
Abstract
Risk and reward are fundamental concepts in the cooperative control of unmanned systems. This paper focuses on a constructive relationship between a cooperative planner and a learner in order to mitigate the learning risk while boosting the asymptotic performance and safety of agent behavior. Our framework is an instance of the intelligent cooperative control architecture (iCCA) where a learner (Natural actor-critic, Sarsa) initially follows a “safe” policy generated by a cooperative planner (consensus-based bundle algorithm). The learner incrementally improves this baseline policy through interaction, while avoiding behaviors believed to be “risky”. This paper extends previous work toward the coupling of learning and cooperative control strategies in real-time stochastic domains in two ways: (1) the risk analysis module supports stochastic risk models, and (2) learning schemes that do not store the policy as a separate entity are integrated with the cooperative planner extending the applicability of iCCA framework. The performance of the resulting approaches are demonstrated through simulation of limited fuel UAVs in a stochastic task assignment problem. Results show an 8% reduction in risk, while improving the performance up to 30%.
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5991309
