An RRT-Based Motion Planning Method for Hyper-Redundant Manipulators in Confined Spaces

Abstract

Hyper-redundant manipulators have redundant degrees of freedom, bringing additional difficulties to the motion planning. Generally, existing motion planning methods generate paths without considering joint angle limitation of hyper-redundant manipulators. And the generated paths are discretized, leading to discrete errors. In this paper, we present an autonomous motion planner to generate paths, which can be followed by hyper-redundant manipulators with analytical solutions. Starting from the ending pose in the workspace, the rapidly exploring random tree can expand to multiple entrances with limited curvature of arc segments, which ensures that the joint angle limitation is satisfied. Meanwhile, the generated path consists of arc segments, which makes the generated paths can be followed with analytical solutions. Several simulations are conducted to demonstrate the aforementioned advantages. For further validation of the planner's effectiveness, a hyper-redundant manipulator system is used to follow the generated path with follow-the-leader motion.