Path planning for cluster unmanned aerial vehicles based on airborne launch
Shuanli Jia, Naiming Qi, He Long, Desong Du, Rui Zhou, Weiran Yao, Yanfang Liu · 2024
The potential applications of airborne launch cluster unmanned aerial vehicles (UAVs)are enormous. However, planning the trajectory of the carrier aircraft to maximize efficiency while ensuring its safety presents a challenging task. This paper addresses a complex combinatorial optimization problem of a single carrier aircraft conducting multiple UAV release tasks. A comprehensive UAV path planning model has been developed to accurately depict the impact of the carrier aircraft's flight path on task effectiveness. Additionally, we propose an improved non-dominated sorting genetic algorithm-II (INSGA-II) algorithm aimed at finding the optimal release points and sequences. By introducing selection operators, hybrid crossover operators, and an enhanced elite retention strategy, we have made various improvements to the NSGA-II algorithm, enhancing its global search capability and performance. Simulation results conducted in two different scenarios thoroughly validate the effectiveness of the proposed algorithm. A comparison with the original NSGA-II algorithm reveals that the INSGA-II algorithm can find better solutions within an acceptable number of iterations.