基于融合A^(*)算法的无人机路径规划研究

Research on UAV path planning based on fusion A^(*)algorithm

针对三维环境下A^(*)算法搜索路径不平滑,不具有动态避障的问题,本文提出了一种融合A^(*)算法。该算法在A^(*)算法的基础上,首先引入了俯仰角和偏航角作为搜索约束,其次采用变权值的评估函数和无人机航程、飞行高度、威胁代价,最后将平滑后的A^(*)算法与人工势场法相结合,并利用粒子群算法对A^(*)算法和人工势场法涉及的参数进行寻优。仿真结果显示,融合算法较传统A^(*)算法而言,节省5.34%的燃油损耗,提高了搜索效率,缩短了路径长度,规划出的路径更加平滑,而且能够实现实时动态避障。

Aiming at the problem that the search path of A^(*)algorithm is not smooth and does not have dynamic obstacle avoidance in 3 D environment, this paper proposes a fusion A^(*)algorithm. Based on the A^(*)algorithm, this algorithm first introduces pitch angle and yaw angle as search constraints, which shortens the time for path planning, and secondly uses variable weight evaluation functions and UAV range, flight height, and threat cost. Finally, the smoothed A^(*)algorithm is combined with the artificial potential field method, and the particle swarm algorithm is used to optimize the parameters involved in the A^(*)algorithm and the artificial potential field method. The simulation results show that compared with the traditional A^(*)algorithm, the fusion algorithm saves 5.34% of fuel consumption, improves the search efficiency, shortens the path length, the planned path is smoother, and can achieve real-time dynamic obstacle avoidance.