RRT-Based Path Planning for Follow-the-Leader Motion of Hyper-Redundant Manipulators
Hanghang Wei, Zheng Yang, Guoying Gu · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2021
Hyper-redundant manipulators with slender body and high dexterity are widely applied for operations in confined spaces. Among the motion planning methods for these operations, the follow-the-leader motion controller is generally developed to avoid the obstacles, while the path trajectories are usually given. In this paper, we present an autonomous motion planner with a specialized rapidly exploring random tree (Sp-RRT) approach for follow-the-leader motion of hyper-redundant manipulators. Starting from the target pose in the workspace, the exploring tree can expand to multiple entrances while guaranteeing the final pose of the manipulator’s end-effector. Meanwhile, the dexterity of hyper-redundant manipulators (even with different segments) can be utilized sufficiently with customized expanding parameters. Simulation results compared with existing methods are conducted to demonstrate the aforementioned characteristics and effectiveness. For further validation, we experimentally verify the development with our custom-built hyper-redundant manipulator to realize the generated path with follow-the-leader motion.