改进PRM算法的无人机航迹规划

Improved PRM algorithm for path planning of UAV

针对传统随机路标图(PRM)算法对航迹规划的自由空间进行随机采样表现出的随机性,和在狭窄通道中可能出现的航迹无解问题,提出一种改进PRM的航迹规划算法。通过将障碍物的边界点作为确定采样点,并对栅格地图的自由空间建立最优可行区域,降低传统PRM算法随机采样点的分散性,使路径搜索具有明确性,以此来提高算法在时间和空间上的利用率。为验证算法的有效性,采用MATLAB仿真平台建立无人机二维和三维空间,进行航迹规划对比实验。实验结果表明:在三维空间中,改进PRM算法较传统PRM算法航迹规划时间降低2.469%至5.721%,航迹长度减少0.89%至1.54%。

Aiming at the randomness of the random sampling of the free space of the path planning and the no solution problem of the possible track in the narrow channel,a path planning algorithm for improving probabilistic road map(PRM)is proposed.By using the boundary point of the obstacle as the sampling point and establishing the optimal feasible area for the free space of the grid map,the algorithm reduces the dispersion of the random sampling points of the traditional PRM algorithm and makes the path search clear,to improve the time and space utilization of the algorithm.In order to verify the effectiveness of the algorithm,the MATLAB simulation platform is used to establish the two-dimensional(2D)and three-dimensional(3D)space of the UAV,and the path planning comparison experiment is carried out.The experimental results show that in the 3D space,the improved PRM algorithm is reduced by 2.469%~5.721%and the length of path is reduced by 0.89%~1.54%compared with the traditional PRM algorithm.