Autonomous navigation of unmanned aerial vehicles (UAVs) for border patrolling: a stochastic framework

Abstract Accepted by: Aris Syntetos 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.