基于改进快速搜索随机树法的机械手路径优化

Path Optimization of Manipulator Based on the Improved Rapidly-exploring Random Tree Algorithm

针对多关节机械手路径优化问题,提出一种改进快速搜索随机树(Rapidly-exploring random trees,RRT)优化算法。利用标准RRT算法规划初始可行路径,根据路径长度与路径安全性计算出该路径代价。在后期搜索树生长过程中,中间目标点并非随机采样,而是选择能使当前路径代价低于其之前路径代价的节点,同时对该节点进行距离检测,避免产生过于密集的节点集。为加快搜索树向未知区域的扩充速度,从最近节点向中间目标点扩充过程中,采用一种贪婪启发式扩充算法:节点以一定步长循环扩充,直至扩充到达目标节点或产生不连通节点。最后对6自由度检修机械手进行路径规划仿真试验,结果表明相对于标准RRT算法,规划路径的质量得到大幅提高。

In view of the problem of path optimization for multi-joint manipulator,an improved RRT（rapidly-exploring random tree） optimization algorithm is proposed.An initial feasible path is searched by using the standard RRT algorithm,then the path cost is calculated on the basis of path length and path security.During the growth of later exploring tree,the middle target node is not randomly sampled,but is selected according to the constraint that it can make the current path cost lower than the former one.Also the selected node should be checked for the distance in order to avoid generating too intensive nodes.For the purpose of speeding up the expansion of the exploring tree toward unknown regions,a greedy heuristic expansion algorithm is used in the process of expanding from the nearest node toward the middle target point,so that the node will expand circularly at a certain step size until it reaches the target node or produces unconnected node.Finally,the path planning simulation experiment is carried out for the 6-DOF maintenance manipulator,the result shows that the planned path quality is greatly improved in comparison to the standard RRT algorithm.