Research on the Adaptability of Unmanned Aerial Vehicle Route Planning Based on Genetic Algorithm in Complex Environments
Jun Zhang, Yifei Zhan, Yuexiang Cao · 2024
This paper proposes a multi-objective Unmanned Aerial Vehicle (UAV) route planning algorithm based on an improved genetic algorithm to address the challenges faced by UAVs in complex environments, such as multiple dynamic obstacles, rapid environmental changes, and difficulties in path optimization. Firstly, based on the task description of UAV route planning and the traditional genetic algorithm model, a UAV route planning model is established to obtain key information such as waypoints, obstacles, and environmental features. Secondly, in order to improve the adaptability and search efficiency of the algorithm, a strategy is proposed to introduce an environmental adaptability term and an adaptive crossover function. Furthermore, based on the characteristics of complex environmental data, an improved genetic algorithm model was constructed that comprehensively considers the impact of environmental factors and UAV performance, providing a more accurate description of the UAV's route planning problem in complex environments. Finally, a UAV route planning system based on an improved genetic algorithm was built, and through simulation and experimental verification, it was proven that the algorithm proposed in this paper can effectively improve the performance of UAV route planning in complex environments.