Path planning for cluster unmanned aerial vehicles based on airborne launch

Shuanli Jia; Naiming Qi; Long He; Desong Du; Rui Zhou; Weiran Yao; Yanfang Liu

doi:10.1117/12.3032462

16 August 2024 Path planning for cluster unmanned aerial vehicles based on airborne launch

Shuanli Jia, Naiming Qi, Long He, Desong Du, Rui Zhou, Weiran Yao, Yanfang Liu

Proceedings Volume 13218, First Aerospace Frontiers Conference (AFC 2024); 132180I (2024) https://doi.org/10.1117/12.3032462
Event: First Aerospace Frontiers Conference (AFC 2024), 2024, Xi’an, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuanli Jia, Naiming Qi, Long He, Desong Du, Rui Zhou, Weiran Yao, and Yanfang Liu "Path planning for cluster unmanned aerial vehicles based on airborne launch", Proc. SPIE 13218, First Aerospace Frontiers Conference (AFC 2024), 132180I (16 August 2024); https://doi.org/10.1117/12.3032462

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