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Effective UAV patrolling for swarm of intruders with heterogeneous behavior

Published online by Cambridge University Press:  08 February 2023

Ali Moltajaei Farid Open the ORCID record for Ali Moltajaei Farid [Opens in a new window] ,
Lim Mei Kuan ,
Md Abdus Samad Kamal  and
KokSheik Wong
Ali Moltajaei Farid*
Affiliation:
Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, Canada
Lim Mei Kuan
Affiliation:
School of Information Technology, Monash University Malaysia, Subang Jaya, Malaysia
Md Abdus Samad Kamal
Affiliation:
Division of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
KokSheik Wong
Affiliation:
School of Information Technology, Monash University Malaysia, Subang Jaya, Malaysia
*
*Corresponding author. E-mail: alifaridm@mail.com
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Abstract

The phenomenal growth in the utilization of commercial unmanned aerial vehicles (UAVs) or drones leads to an urgent need for new approaches to ensure safety in the sky. Effective aerial surveillance requires patrolling swarms to react according to the various behaviors demonstrated by intruding swarms, but existing approaches are not practical when dealing with a large number of drones. Specifically, predicting the behaviors or planned paths of the intruding swarms is highly challenging as intruders may perform evasive strategies to avoid detection. Therefore, this work utilizes heuristic search strategies and investigates how various intruder behaviors affect the search performance. To investigate the search performance, a swarm versus swarm simulator is developed. Using the simulator, first, a comparative study is performed to evaluate how intruders’ behaviors can affect the performance of the patrolling swarm. Subsequently, three approaches, including single-objective optimization, multi-objective optimization, and Lévy flight, are compared in terms of their detection performance in a bounded space. The results suggest that multi-objective optimization outperforms both single-objective optimization and Lévy flight-based approaches. Furthermore, our results show that intruders have a lower chance of being tracked when moving in a dense crowd, and this finding reaffirms the schooling behaviors of fish. In a specific simulation scenario, the total percentage of detection is above 90%. However, the detection percentage is highly related to other factors such as search space, number of patrolling UAVs, and the intruders’ behaviors.

Keywords

swarm UAVsswarm search
Type
Research Article
Information
Robotica , Volume 41 , Issue 6 , June 2023 , pp. 1673 - 1688
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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