To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction,an improved bi-directional rapidly-exploring random tree algorith...To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction,an improved bi-directional rapidly-exploring random tree algorithm based on greedy growth strategy in 3D space is proposed.The workspace of manipulator established based on Monte Carlo method is used as the sampling space of the rapidly-exploring random tree,and the opposite expanding greedy growth strategy is added in the random tree expansion process to improve the path planning efficiency.Then the generated path is reversely optimized to shorten the length of the planned path,and the optimized path is interpolated and pose searched in Cartesian space to form a collision-free optimized path suitable for humanoid manipulator motion.Finally,the validity and reliability of the algorithm are verified in an intelligent elderly care service scenario based on Walker2,a large humanoid service robot.展开更多
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...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。展开更多
基金The Key-Area Research and Development Program of Guangdong Province,China(No.2019B010154003)。
文摘To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction,an improved bi-directional rapidly-exploring random tree algorithm based on greedy growth strategy in 3D space is proposed.The workspace of manipulator established based on Monte Carlo method is used as the sampling space of the rapidly-exploring random tree,and the opposite expanding greedy growth strategy is added in the random tree expansion process to improve the path planning efficiency.Then the generated path is reversely optimized to shorten the length of the planned path,and the optimized path is interpolated and pose searched in Cartesian space to form a collision-free optimized path suitable for humanoid manipulator motion.Finally,the validity and reliability of the algorithm are verified in an intelligent elderly care service scenario based on Walker2,a large humanoid service robot.
文摘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。
