摘要
针对RRT算法在路径规划中随机性强、无效节点多等缺点,设计了一种分区采样的路径规划方法。该方法首先以地图的长边与短边的坐标值为参考,将地图分为若干区域,随机采样点的最近点限制在区域内查找,提高遍历速度;其次,以首次进入下一区域的节点为根节点进行搜索树的扩展,为防止节点在分区边界局部震荡,提出了一种节点试采样策略,允许随机树中有限节点进入上一采样区域;再者在节点扩展方面,采用随机方式决定一次采样扩展步长次数;最后将三个随机树连接,将冗余点剪除,采用二次贝塞尔曲线对路径进行优化。实验表明:该方法生成路径节点少、效率高、导向性强。
Aiming at the shortcomings of RRT algorithm in path planning,such as strong randomness and many invalid nodes,a path planning method based on partition sampling is designed.First of all,with reference to the coordinate values of the long side and the short side of the map,the map is divided into several regions,and the nearest point of the random sampling point is limited to the region to improve the traversal speed.Secondly,the node entering the next region for the first time is used as the root node to expand the search tree,in order to prevent the node from shaking locally at the partition boundary,a node trial sampling strategy is proposed,which allows the limited nodes in the random tree to enter the previous sampling area.Furthermore,in terms of node expansion,a random method is used to determine the number of sampling expansion steps in a single instance.Finally,the three random trees are connected,the redundant points are cut off,and a quadratic Bézier curve is used to optimize the path.The experiment shows that this method generates fewer path nodes,has high efficiency,and strong directionality.
作者
韩金利
HAN Jin-li(Department of Numerical Control Engineering,Shanxi Institute of Mechanical&Electrical Engineering,Changzhi 046011,China)
出处
《机械工程与自动化》
2023年第6期31-33,共3页
Mechanical Engineering & Automation
基金
山西省高等学校科技创新项目(2020L0760)。
