With the increase of mission requirements, multi-Unmanned Aerial Vehicle cooperative work has been widely used in many fields. However, when performing tasks in a complex environment, how to quickly plan the collaborative flight path of multi-UAV is always a challenging research topic. Aiming at the problem that the traditional genetic algorithm has a single way of connecting path points in the process of path planning, it is difficult to adapt to the complex environment and the long path length, this paper proposes an improved genetic algorithm. Wherein, the Rapidly-Exploring Random Tree is used as the initial path scheme and insertion operator. Then, the proposed algorithm introduces a deletion operator to increase the path length, removes the crossover operator which has little contribution to the path optimization, and takes the trajectory of other UAVs in the cluster as the constraint condition in the planning. Finally, the improved genetic algorithm is applied to the cooperative path planning of multi-UAV. The simulation results show that compared with the traditional genetic algorithm, the path length of the improved genetic algorithm is reduced by 38%, and the time consumption of the improved genetic algorithm is reduced by 77%.
Multi-UAV Collaborative Path Planning Based on Improved Genetic Algorithm
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Kapitel : 260 ; 2648-2657
18.03.2022
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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