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A Hybrid PSO Algorithm Based Flight Path Optimization for Multiple Agricultural UAVs

机译：基于混合PSO算法的多种农业无人机航路优化

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摘要

Unmanned aerial vehicles (UAVs) has shown an increasing interests in agricultural applications. However, single UAV is considered impractical for its limited flight endurance. In this paper, A VND (Variable Neighborhood Descend) enhanced Genetic-PSO (Particle Swarm Optimization) algorithm is applied to optimize the flight paths for a group of Multiple agricultural UAVs. Instead of minimizing the total flight distance (approach A), the objective of our method is to optimize the flight paths of the whole UAVs group with minimum make-span (approach B). Both approaches have been verified respectively in two agricultural regions of Shaanxi Province. The comparative results show that our proposed method (approach B) effectively reduced the UAVs group's flight time and make it better serve the precision agriculture.
机译：无人机（UAV）在农业应用中表现出越来越高的兴趣。然而，单个无人机由于其有限的飞行耐久性而被认为是不切实际的。本文采用了一种VND（可变邻域下降）增强型遗传PSO（粒子群优化）算法来优化一组多架农业无人机的飞行路径。不是最小化总飞行距离（方法A），我们方法的目的是以最小的跨度（方法B）来优化整个无人机组的飞行路径。两种方法都分别在陕西省的两个农业地区得到了验证。比较结果表明，我们提出的方法（方法B）有效地减少了无人机群的飞行时间，使其更好地服务于精确农业。

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