基于安全场改进RRT^*算法的智能汽车路径规划方法

Safety Field-based Improved RRT^*Algorithm for Path Planning of Intelligent Vehicle

快速搜索随机树(rapidly-exploring random tree,RRT)算法是智能汽车路径规划的常用方法,但传统RRT和RRT^*算法存在路径抖动大、易陷入局部区域和计算效率低等缺点。针对这些问题,本文中结合实车数据提出了一种基于安全场改进RRT^*算法的智能汽车路径规划方法。首先,建立了基于安全距离模型的安全场,通过驾驶数据采集试验对模型关键参数进行了提取;在此基础上,提出了具备安全场引导和角度约束等策略的改进RRT^*算法;最后,通过仿真对算法进行了验证。结果表明,本文提出的路径规划方法能计算出满足车辆轨迹曲率约束的有效路径,同时具有较快的搜索速度和更高的成功率。

Rapidly-exploring random tree(RRT)algorithm is a common algorithm for path planning of intelligent vehicle.But traditional RRT and RRT^*algorithms have disadvantages of large path jitter,easy to fall into local region and low calculation efficiency.In view of these problems,an improved RRT^*algorithm for the path planning of intelligent vehicle based on safety field and real vehicle driving data is proposed in this paper.Firstly,a safety field based on safety distance model is established,and the key parameters of the model are extracted through driving data acquisition test.On this basis,an improved RRT^*algorithm with safety field guidance and angle constraint strategies is proposed.Finally,the algorithm is verified by simulation.The results show that the path planning method proposed can calculate the effective path meeting the curvature constraint of vehicle trajectory with faster search speed and higher success rate.