基于CHC算法的无人机航迹规划方法

利用改进的遗传算法——跨世代异物种重组大变异(CHC, Cross generation Heterogeneous recombination Cataclysmic mutation)算法提出了一种无人机的航迹规划方法.初始种群即初始航线集利用具有启发式信息的搜索算法产生;适应度函数为距离指标与威胁指标的组合形式;选择操作群体为当前群体与上世代群体的群体总和,由于大个体群操作,可以更好地保持遗传多样性;交叉操作采用单点交叉方法,交叉点取为2条航线中距离最近的2个点;变异操作的步骤是:首先在航线中搜索出2个点,然后算出这2个点之间的直线距离与实际航线距离的比值,如果这个比值小于某一阈值则以这2个点为端点重新规划一条航线.由于考虑到了无人机约束条件的限制,从而避免了盲目性且加快了收敛速度.仿真结果表明该方法比基本遗传算法要快而且满足最优条件. 

Path planning method of UAV based on CHC algorithm

An approach of UAV(unmanned aerial vehicle) path planning based on the improved genetic algorithm CHC(cross generation heterogeneous recombination cataclysmic mutation) algorithm was proposed. The initial population was produced by the searching algorithms containing heuristic information. The fitness function was the combination of distance and menace index. The select operating population was summation of current and previous one, for the operating population was big, the genetic diversity could be kept better. The single node crossover was used in cross operating, and the nearest two ones of two lines were choosen as the cross nodes. The mutate operating process was that two nodes were searched first, then the ratio of the two nodes′ linear distance to the two nodes′ real distance along the path was calculated. If the ratio was less than a fixed value then a new route between the two nodes was planned. Due to the restrict of the UAV′s capability, the algorithm can avoid blindness and can speed up the constringency. Simulation results show that the planning algorithm is faster than the basal genetic algorithms and meet the optimal requirements.