A competitive Predator–Prey approach to enhance surveillance by UAV swarms

STOLFI ROSSO, Daniel; Brust, Matthias R.; Danoy, Grégoire; Bouvry, Pascal

doi:10.1016/j.asoc.2021.107701

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A competitive Predator–Prey approach to enhance surveillance by UAV swarms

STOLFI ROSSO, Daniel; Brust, Matthias R.; Danoy, Grégoire et al.

2021 • In Applied Soft Computing, 111, p. 107701

Peer reviewed

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https://hdl.handle.net/10993/49053

DOI
10.1016/j.asoc.2021.107701

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Keywords :

Swarm robotics; Computer simulation; Mobility model; Unmanned aerial vehicle; Competitive coevolutionary genetic algorithm

Abstract :

[en] In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator–Prey approach. We propose a Competitive Coevolutionary Genetic Algorithm (CompCGA) which optimises the parameters of the UAVs (i.e. predators) to maximise the detection of intruders, while the parameters of the intruders (i.e. preys) are optimised to maximise their intrusion success rate. Having chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) as the base mobility model for the UAVs, we propose an improved new version, where its behaviour is modified by identifying and optimising new parameters to improve the overall success rate when detecting intruders. Six case studies have been optimised using simulations by performing 30 independent runs (180 in total) of our CompCGA. Finally, we conducted a series of master tournaments (1,800,000 evaluations) using the best specimens obtained from each run and case study to test the robustness of our proposed approach against unexpected intruders. Our surveillance system improved the average percentage of intruders detected with respect to CACOC by a maximum of 126%. More than 90% of intruders were detected on average when using a swarm of 16 UAVs while CACOC’s detection rates are always under 80% in all cases.

Disciplines :

Computer science

Author, co-author :

STOLFI ROSSO, Daniel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG

Brust, Matthias R. ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG

Danoy, Grégoire ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

Bouvry, Pascal ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

External co-authors :

Language :

English

Title :

A competitive Predator–Prey approach to enhance surveillance by UAV swarms

Publication date :

2021

Journal title :

Applied Soft Computing

ISSN :

1568-4946

Volume :

111

Pages :

107701

Peer reviewed :

Peer reviewed

Additional URL :

https://www.sciencedirect.com/science/article/pii/S1568494621006220

Name of the research project :

HUNTED

Funders :

ONRG

Available on ORBilu :

since 16 December 2021

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