Autonomous navigation of unmanned aerial vehicles (UAVs) for border patrolling: a stochastic framework
Autonomous navigation of unmanned aerial vehicles (UAVs) for border patrolling: a stochastic framework
This study focuses on the utilization of unmanned aerial vehicles (UAVs) in internal safety operations, specifically border patrolling. The objective is to explore a stochastic navigation strategy for UAVs that maximizes the probability of success in the face of uncertainty in intruder movement. A fully autonomous UAV algorithm is developed and tested through simulation in border violation scenarios. The algorithm enables the UAV to autonomously search, pursue and defend the area against intruders. By utilizing simulation optimization methods as simulated annealing, stochastic Nelder Mead and radial basis function, the movement strategy of the UAV is discovered in stochastic environments. The results showcase the effectiveness of the algorithms in scenarios where limited information about the intruder’s movement is available. The developed mathematical tools hold applicability in various real-life contexts, in defence and security operations. This research contributes to the field of surveillance strategies by presenting a sophisticated approach to enhance UAV navigation in uncertain environments, ultimately improving mission success rate.
UAV path planning, border patrol, mission success rate, simulation, stochastic movement
231-254
Biskin, Busra
e9d4caed-f29d-4524-b85e-9f4d9f42287c
Fliege, Jörg
54978787-a271-4f70-8494-3c701c893d98
Martinez-Sykora, Antonio
2f9989e1-7860-4163-996c-b1e6f21d5bed
11 February 2025
Biskin, Busra
e9d4caed-f29d-4524-b85e-9f4d9f42287c
Fliege, Jörg
54978787-a271-4f70-8494-3c701c893d98
Martinez-Sykora, Antonio
2f9989e1-7860-4163-996c-b1e6f21d5bed
Biskin, Busra, Fliege, Jörg and Martinez-Sykora, Antonio
(2025)
Autonomous navigation of unmanned aerial vehicles (UAVs) for border patrolling: a stochastic framework.
IMA Journal of Management Mathematics, 36 (2), , [dpae033].
(doi:10.1093/imaman/dpae033).
Abstract
This study focuses on the utilization of unmanned aerial vehicles (UAVs) in internal safety operations, specifically border patrolling. The objective is to explore a stochastic navigation strategy for UAVs that maximizes the probability of success in the face of uncertainty in intruder movement. A fully autonomous UAV algorithm is developed and tested through simulation in border violation scenarios. The algorithm enables the UAV to autonomously search, pursue and defend the area against intruders. By utilizing simulation optimization methods as simulated annealing, stochastic Nelder Mead and radial basis function, the movement strategy of the UAV is discovered in stochastic environments. The results showcase the effectiveness of the algorithms in scenarios where limited information about the intruder’s movement is available. The developed mathematical tools hold applicability in various real-life contexts, in defence and security operations. This research contributes to the field of surveillance strategies by presenting a sophisticated approach to enhance UAV navigation in uncertain environments, ultimately improving mission success rate.
More information
Accepted/In Press date: 5 February 2025
e-pub ahead of print date: 6 February 2025
Published date: 11 February 2025
Keywords:
UAV path planning, border patrol, mission success rate, simulation, stochastic movement
Identifiers
Local EPrints ID: 499501
URI: http://eprints.soton.ac.uk/id/eprint/499501
ISSN: 1471-678X
PURE UUID: 720d30a9-4644-4d76-ba31-6c06561092a9
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Date deposited: 21 Mar 2025 17:51
Last modified: 22 Aug 2025 02:05
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Author:
Busra Biskin
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