NSGA-II Algorithm in Path Planning of Multiple Unmanned Aerial Vehicle Collaborative Search Tasks
Xin Huang · 2024
Abstract: In response to the problem of single objective optimization in collaborative search path planning for multiple unmanned aerial vehicles, this paper aims to use the NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm for optimization. The first step of this article is to conduct multi-objective optimization modeling on the path planning problem, clarifying that the optimization objectives include search area coverage and flight time. To find the best path planning solution, the NSGA-II algorithm is used in the second stage to enhancing the dynamic environment's adaptability and the drone's flexibility and search efficiency in complex surroundings constitute the third step. Finally, by comparing the performance indicators with traditional algorithms, it was found that the algorithm used in this paper performs well in terms of search area coverage, with the highest average coverage, up to 87%. At the same time, it requires the least flight time to perform search tasks. In summary, the multi-objective optimization method based on NSGA-II algorithm proposed in this article has shown significant performance advantages in multi-UAV (Unmanned Aerial Vehicle) collaborative search path planning.