无人机航路规划问题的高效求解是确保无人机飞行效率和飞行安全的关键.针对无人机三维航路规划求解效率低的问题,以足球队训练算法(football team training algorithm,FTTA)为基础,提出了一种基于改进FTTA的无人机航路规划方法.该方法结合了无人机飞行航路的实际要求,以航路代价、高度约束和转弯约束3个指标为核心,构造了多约束条件下无人机三维航路规划模型的适应度函数.并以典型三维航路规划问题为求解对象,从航路代价、迭代次数、算法运行时间3个评价指标出发,研究了随机概率、学习概率和通信概率对算法求解效率的影响,与鲸鱼优化算法(whale optimization algorithm,WOA)进行了对比实验.结果表明,改进后的算法在求解无人机三维航路规划时具有较高的求解效率.
UAV Path Planning Based on Improved Football Team Training Algorithm
The efficient solution to Unmanned Aerial Vehicle(UAV)path planning problem is a key to ensure UAV flight efficiency and flight safety.To efficiently solve the problem of UAV 3D path planning,a method based on improved football team training algorithm(FTTA)is proposed.With the actual re-quirements of UAV flight path considered,the fitness function of UAV 3D path planning model under multiple constraints is constructed,which takes path cost,height constraint and turn constraint into ac-count.Then,with a typical 3D path planning problem as the solution object,the effects of three parame-ters,which are stochastic probability,learning probability and communications probability,are studied on the efficiency of the algorithm considering three evaluation indexes,which are path costs,iteration times and algorithm running time,respectively.Finally,a comparison experiment is conducted with the whale optimization algorithm,and the results show that the improved algorithm has a better solving efficiency.
path planningcollective traininggroup trainingindividual extra trainingfitness function
万方数据
维普
