Sampling-Based Obstacle Avoidance Path Planning Algorithm for Nuclear Industry Manipulators in Narrow Environments - Huang, Ge; Liu, Guanyang; Niu, Yuanzhen; Wu, Dehui; Shen, Chenlin (IEEE/ASME transactions on mechatronics, 2025)
This article presents an efficient obstacle avoidance path planning algorithm for robotic manipulators, termed Bi-Balanced Rapidly-exploring Random Vine (BBRRV), specifically designed for operational environments in the nuclear industry. The BBRRV algorithm guides the sampling process within an Rapidly-exploring Random Tree (RRT)-based framework using the wrist point position and incorporates a passive compliance control strategy to maintain the pose stability of the end-effector. By balancing exploration and exploitation during sampling, the algorithm dynamically adjusts its behavior according to the current extension state. In cases of extension failure, principal component analysis is employed to reorient the extension direction, thereby improving pathfinding performance in constrained environments. Furthermore, a bi-tree extension strategy is introduced to overcome directional extension limitations. Simulation tests and physical experiments with a compact manipulator demonstrate that BBRRV can efficiently compute feasible path in nuclear-relevant environments containing typical hole-shaped obstacles, exhibiting clear advantages over conventional methods.