基于改进遗传算法的无人机航路规划

UAV route planning based on the improved genetic algorithm

针对已知源-目标的最短最优航路规划问题,首先对无人机航路规划相关约束及算法仿真环境进行了综合分析,构建了航路规划算法仿真环境,明确了无人机的性能约束,进而提出了一种可以融合多种约束条件的航路评价函数;然后,针对遗传算法存在的早熟收敛以及后期搜索迟钝等问题,考虑其问题之间存在的耦合关系,提出了适应度值标定、种群多样化和精英保留策略的融合改进方案。实验结果表明改进的遗传算法会节省约11.8%的燃油损,同时无人机机动转弯相对更少,提高了无人机飞行的安全性和高效性。

Aiming at the shortest optimal route planning,firstly,this paper comprehensively analyzes the constraints and simulation environment of UAV route planning,constructs the route planning algorithm simulation environment,defines the performance constraints of UAV,and then proposes a route evaluation function that can integrate multiple constraints.Then,aiming at the problems of local optimum and slow convergence of genetic algorithm,considering the coupling relationship between the problems,a fusion improvement scheme of fitness value calibration,population diversification and elite retention strategy is proposed.The experimental results show that the improved genetic algorithm can save about 11.8%fuel loss,and the UAV has relatively fewer maneuvers,which improves the flight safety and efficiency of UAV.