基于动态变采样区域RRT的无人车路径规划

针对无人车传统RRT路径规划算法节点搜索盲目性、随机性以及路径曲折不连续等问题,提出一种动态变采样区域RRT路径规划算法(dynamic variable sampling area RRT, DVSA-RRT).首先,初始化地图信息,根据动态变采样区域公式划分采样空间,进而选择采样区域;在此基础上,利用基于安全距离的碰撞检测、概率目标偏置策略和多级步长扩展完成初始路径规划;最后,利用考虑最大转角约束的逆向寻优和3次B样条曲线对初始路径进行拟合优化.仿真结果表明,该算法相较于原始RRT算法在不同地图环境下的搜索时间和采样次数均降低50%以上,大大降低了节点搜索的盲目性和随机性,相较于其他算法搜索时间也减少30%以上,且优化后的路径平滑满足车辆运动动力学约束.

Path planning of unmanned vehicle based on dynamic variable sampling area RRT

Aiming at the blindness, randomness of node search, and the discontinuous path planning in the traditional RRT path planning algorithm for unmanned vehicles, a dynamic variable sampling area RRT(DVSA-RRT) path planning algorithm is proposed. Firstly, the map information is initialized. The sampling space is divided according to the dynamic variable sampling area formula. Then the sampling area is selected. The initial path planning is completed by using collision detection based on safe distance, the probabilistic target offset strategy and multi-level step expansion. Finally, the inverse optimization considering the maximum rotation constraint and cubic B-spline curve is used to fit and optimize the initial path. Simulation results show that compared with the original RRT algorithm, the search time and sampling times of the proposed algorithm in different map environments are reduced by more than 50%, which greatly reduces the blindness and randomness of node search. Compared with other algorithms, the search time of the proposed algorithm is also reduced by more than 30%. The planned path is smooth and meets the constraints of vehicle dynamics.