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2020 | OriginalPaper | Chapter

Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection

Authors : Daniel H. Stolfi, Matthias R. Brust, Grégoire Danoy, Pascal Bouvry

Published in: Optimization and Learning

Publisher: Springer International Publishing

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Abstract

In this paper we present the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model applied to Unmanned Aerial Vehicles (UAV) in order to perform surveillance tasks. The use of unpredictable routes based on the chaotic solutions of a dynamic system as well as pheromone trails improves the area coverage performed by a swarm of UAVs. We propose this new application of CACOC to detect intruders entering an area under surveillance. Having identified several parameters to be optimised with the aim of increasing intruder detection rate, we address the optimisation of this model using a Cooperative Coevolutionary Genetic Algorithm (CCGA). Twelve case studies (120 scenarios in total) have been optimised by performing 30 independent runs (360 in total) of our algorithm. Finally, we tested our proposal in 100 unseen scenarios of each case study (1200 in total) to find out how robust is our proposal against unexpected intruders.

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Title: Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection
Authors: Daniel H. Stolfi
Matthias R. Brust
Grégoire Danoy
Pascal Bouvry
Publisher: Springer International Publishing
Book: Optimization and Learning
Print ISBN: 978-3-030-41912-7

Electronic ISBN: 978-3-030-41913-4

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-41913-4_4

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