【摘要】 基于快速扩展随机树(RRT)算法对无人驾驶车辆路径规划问题进行研究。对无人驾驶车辆路径规划进行了定义,建立了车辆的运动学模型,给出了车辆路径规划的标准。结合人工势场法提出了RRT和人工势场联合的无人驾驶车辆路径规划算法,并从平均采样节点数、路径规划时间、规划路径平均长度、规划路径最大曲率4个方面与RRT算法进行了对比,仿真结果表明, RRT与人工势场联合算法的性能优于RRT算法。更多还原

【Abstract】 It studies the path planning problem of driverless vehicles based on the fast-extended random tree(RRT) algorithm. It gives the definition of driverless vehicle path planning, establishes the vehicle kinematics model, and describes the standard of vehicle path planning. Combined with the artificial potential field method, it proposes a path planning algorithm for unmanned vehicles based on RRT and artificial potential field. The algorithm is compared with RRT algorithm from four aspects of average sampling node number, path planning time, average length of planned path and maximum curvature of planned path. The simulation results show that the performance of the joint algorithm of RRT and artificial potential field are better than that of RRT algorithm.更多还原