Co-evolutionary Search Path Planning under Constrained Information-Sharing for a Cooperative Unmanned Aerial Vehicle Team

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Authors
  1. Berger, J.
  2. Happe, J.
Corporate Authors
Defence R&D Canada - Valcartier, Valcartier QUE (CAN);MacDonald Dettwiler and Associates Ltd, Richmond BC (CAN)
Abstract
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.
Report Number
DRDC-VALCARTIER-SL-2010-252 — Scientific Literature
Date of publication
01 Jul 2010
Number of Pages
8
DSTKIM No
CA035023
CANDIS No
534645
Format(s):
Electronic Document(PDF)

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