UAV Path Planning Based on Improved Genetic Algorithm
Baihe Chen,Songlin Lai,Chengbin Chen,Peng Shu,Chen Sifan,Ziye Lai,Li Xu +6 more
- 24 Sep 2021
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TL;DR: In this article, a genetic algorithm-based path planning for UAVs in autonomous cruise operations is proposed to ensure that UAV can choose the most efficient and reliable flight route in complex environments.
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Abstract: In recent years, unmanned aerial vehicles (UAVs) have been widely used in autonomous cruise missions such as high-altitude aerial photography, power inspection, and geographic mapping with the advantages of flexible maneuvering and high stability. In order to ensure that UAVs can choose the most efficient and reliable flight route in complex environments, this paper designs a genetic algorithm-based path planning for UAVs in autonomous cruise operations. The main contents include: 1. Risk assessment of the route using the return value of the fitness function as the safety factor, and solving the optimal path between two points using an improved genetic algorithm; 2. The selection of strategies for multiple landing points under different conditions is proposed to form the optimal path for U AV cruising within range. Through the path planning verification experiments in Matlab, the results show that the improved genetic algorithm has a shorter total path distance compared with the traditional genetic algorithm, and the results have good feasibility and effectiveness.
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