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Co-evolutionary search path planning under constrained information-sharing for a cooperative unmanned aerial vehicle team

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2 Author(s)
Berger, J. ; Defence R&D Canada - Valcartier, Quebec City, QC, Canada ; Happe, J.

Mobile cooperative sensor networks are increasingly used for surveillance and reconnaissance tasks to support domain picture compilation. However, efficient distributed information gathering such as target search by a team of autonomous unmanned aerial vehicles (UAVs) remains very challenging in constrained environment. In this paper, we propose a new approach to learn resource-bounded multi-agent coordination for a multi-UAV target search problem subject to stringent communication bandwidth constraints in a dynamic uncertain environment. It relies on a new information-theoretic co-evolutionary algorithm to solve cooperative search path planning over receding horizons, providing agents with mutually adaptive and self-organizing behavior. The anytime coordination algorithm is coupled to a divergence-based information-sharing policy to exchange high-value world-state information under limited communication bandwidth. Computational results show the value of the proposed approach in comparison to a well-known reported technique.

Published in:

Evolutionary Computation (CEC), 2010 IEEE Congress on

Date of Conference:

18-23 July 2010

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