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A hybrid approach based on multi-agent geosimulation and reinforcement learning to solve a UAV patrolling problem

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5 Author(s)
Perron, J. ; NSim Technol., QC, Canada ; Hogan, J. ; Moulin, B. ; Berger, J.
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In this paper we address a dynamic distributed patrolling problem where a team of autonomous unmanned aerial vehicles (UAVs) patrolling moving targets over a large area must coordinate. We propose a hybrid approach combining multi-agent geosimulation and reinforcement learning enabling a group of agents to find near optimal solutions in realistic geo-referenced virtual environments. We present the COLMAS system which implements the proposed approach and show how a set of UAV can automatically find patrolling patterns in a dynamic environment characterized by unknown obstacles and moving targets. We also comment the value of the approach based on limited computational results.

Published in:

Simulation Conference, 2008. WSC 2008. Winter

Date of Conference:

7-10 Dec. 2008

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