Path Planning for End-Effector-Guided Motion of Tapered Cable-Driven Hyper-Redundant Manipulators

This article presents an improved maximum deflection angle rapidly-exploring random tree Star (IMDA-RRT*) algorithm for path planning in the end-effector-guided motion (EGM) of tapered cable-driven hyper-redundant manipulators (TCDHMs). First, the relationship between the boundaries of additional collision regions arising from the manipulator's EGM and path parameters—such as corner angle, step size, and link length—is analyzed. On this basis, the existing path collision detection algorithm is enhanced to improve path reliability and expand the feasible search space. Furthermore, based on the MDA-RRT* algorithm, improvements are made in terms of constant step size and directional guidance, addressing the issues of inconsistent step sizes caused by the algorithm's pruning process and the inability to constrain the terminal orientation. Finally, simulation and physical experiments are conducted under three distinct scenarios to validate the algorithm's performance. The results demonstrate that the IMDA-RRT* effectively addresses the EGM path planning problem for TCDHMs, showing excellent adaptability in narrow and constrained environments. Compared to existing algorithms, it achieves a higher success rate and faster solving efficiency.