Unmanned aerial vehicle mountain path planning based on improved genetic algorithm
Xueying Zhao, Haolun Zhang, Yufeng Li · 2024
Aiming at the path planning problem of unmanned aerial vehicles in low altitude flight in mountainous terrain, an improved genetic algorithm based on the idea of group parallel exchange is proposed. Firstly, by adopting a point wise planning approach, followed by a scatter fitting strategy, a smooth planning path can be obtained. In the experiment, the advantages of the proposed algorithm in terms of path length were demonstrated by comparing it with other algorithms. The results indicate that the proposed algorithm is not only theoretically feasible, but also achieves the expected results in simulation experiments, effectively solving the problem of the original genetic algorithm easily falling into local optima.