Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection
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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.
KeywordsSwarm robotics Mobility model Unmanned Aerial Vehicle Evolutionary Algorithm Surveillance
This work relates to Department of Navy award N62909-18-1-2176 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein. This work is partially funded by the joint research programme UL/SnT-ILNAS on Digital Trust for Smart-ICT. The experiments presented in this paper were carried out using the HPC facilities of the University of Luxembourg  – see https://hpc.uni.lu.
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