Zhang Xiqing, Wang Pengyu, Guo Yongrui, Han Qianqian, Zhang Kuoran
School of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Smart Transportation Laboratory in Shanxi Province, Taiyuan 030024, China.
Sensors (Basel). 2025 Jan 8;25(2):328. doi: 10.3390/s25020328.
Aiming at the problems of a six-degree-of-freedom robotic arm in a three-dimensional multi-obstacle space, such as low sampling efficiency and path search failure, an improved fast extended random tree (RRT*) algorithm for robotic arm path planning method (abbreviated as HP-APF-RRT*) is proposed. The algorithm generates multiple candidate points per iteration, selecting a sampling point probabilistically based on heuristic values, thereby optimizing sampling efficiency and reducing unnecessary nodes. To mitigate increased search times in obstacle-dense areas, an artificial potential field (APF) approach is integrated, establishing gravitational and repulsive fields to guide sampling points around obstacles toward the target. This method enhances path search in complex environments, yielding near-optimal paths. Furthermore, the path is simplified using the triangle inequality, and redundant intermediate nodes are utilized to further refine the path. Finally, the simulation experiment of the improved HP-APF-RRT* is executed on Matlab R2022b and ROS, and the physical experiment is performed on the NZ500-500 robotic arm. The effectiveness and superiority of the improved algorithm are determined by comparing it with the existing algorithms.
针对六自由度机器人手臂在三维多障碍物空间中存在的采样效率低、路径搜索失败等问题，提出了一种用于机器人手臂路径规划方法的改进快速扩展随机树（RRT*）算法（简称为HP-APF-RRT*）。该算法每次迭代生成多个候选点，基于启发式值概率性地选择采样点，从而优化采样效率并减少不必要的节点。为了缓解在障碍物密集区域搜索时间增加的问题，集成了人工势场（APF）方法，建立引力场和斥力场，引导障碍物周围的采样点朝向目标。该方法增强了在复杂环境中的路径搜索能力，产生接近最优的路径。此外，利用三角不等式简化路径，并使用冗余中间节点进一步优化路径。最后，在Matlab R2022b和ROS上对改进后的HP-APF-RRT*进行了仿真实验，并在NZ500-500机器人手臂上进行了物理实验。通过与现有算法比较，确定了改进算法的有效性和优越性。
