基于CRS-RRT算法的无人机航迹规划

UAV Path Planning Based on CRS-RRT Algorithm

针对快速搜索随机树(RRT)算法在航迹规划过程中存在采样点扩展随机性强、航迹曲折不平滑等问题,提出了一种基于约束随机采样点的RRT(Constrained Random Sampling-based RRT,CRS-RRT)算法。该算法引入人工势场法中的引力场势能函数约束随机采样点在目标点附近采样,引导随机树朝着目标点生长,提高算法的规划速度,并结合去除冗余节点策略和Minimum Snap航迹平滑方法,在复杂三维环境中可快速生成一条安全、平滑且满足无人机动力学约束的航迹。仿真结果表明,该算法有效提高航迹规划速度并缩短航迹长度。

Aiming at the problems of strong randomness of sampling point expansion and unsmooth path in the path planning process of RRT(Rapid-exploration Random Tree)algorithm,a constrained random sampling based RRT algorithm-CRS-RRT(Constrained Random Sampling-based RRT)is proposed.In this method,the potential energy function of gravitational field in artificial potential field method is introduced to constrain random sampling points to sample near the target point,so as to guide the random tree to grow towards the target point and improve the planning speed of the algorithm.Combined with the strategy of removing redundant nodes and Minimum Snap track smoothing method,a safe and smooth track that meets the dynamic constraints of UAV can be quickly generated in a complex three-dimensional environment.The simulation results show that the algorithm can effectively improve the speed of trajectory planning and shorten the trajectory length.