基于改进哈里斯鹰算法的无人飞行器路径规划

Unmanned aerial vehicles path planning based on improved Harris hawks optimization algorithm

针对无人飞行器三维路径规划问题,提出一种基于哈里斯鹰优化(Harris hawks optimization,HHO)算法的无人飞行器三维路径规划算法。首先根据路径规划代价指标和无人飞行器自身性能,建立路径规划模型确立代价函数和约束条件。接着针对传统HHO算法的不足,引入非线性能量因子来平衡全局搜索和局部搜索的关系,使算法避免陷入局部最小值;引入混沌映射对HHO算法进行初始化种群并对其进行局部混沌搜索,增强算法种群多样性和搜索能力。最后通过仿真实验证明,改进的哈里斯鹰优化(improvement Harris hawks optimization,IHHO)算法可以有效规划出安全的无人飞行器航线,并且能够跳出局部最小值和具备较优的收敛速度。

Aiming at the 3D path-planning problem of unmanned aerial vehicles,this paper proposes a 3D path-planning algorithm for unmanned aerial vehicles based on the improved Harris hawks optimization(HHO).First,according to the path-planning cost index and the performance of the unmanned aerial vehicle itself,the path-planning model is built to establish the cost function and constraints.Then,aiming at the shortcomings of the traditional Harris hawks optimization,a nonlinear energy factor is introduced to balance the relationship between global search and local search,lest the algorithm fall into a local minimum;The chaos mapping is introduced to initialize the population of the Harris hawks optimization and perform local chaotic search on it,so as to enhance the population diversity and search capabilities of the algorithm.Finally,it is proved through simulation experiment that improved Harris hawks optimization can effectively plan a safe unmanned aerial vehicles route.And it can jump out of the local minimum,having a better convergence speed.