E-RRT*: Path Planning for Hyper-Redundant Manipulators
Hongcheng Ji, Haibo Xie, Cheng Wang, Huayong Yang
Abstract
A hyper-redundant manipulator(HRM) can flexibly accomplish tasks in narrow spaces. However, its excessive degrees of freedom pose challenges for path planning. In this letter, an ellipsoid-shape rapidly-exporing random tree (E-RRT*) method is proposed for path planning of HRMs in workspace, particularly those with angle limits. This method replaces line segments with ellipsoids to connect adjacent nodes. Firstly, an analysis of angle constraints of the HRM is conducted, providing restrictions on node selection during path planning. Secondly, a slow-speed informed guiding approach is introduced to optimize the sampling process. Finally, the obtained path is enhanced by adding control points and applying cubic polynomial interpolation to achieve path smoothing. Simulations demonstrate that the proposed E-RRT* method effectively solves the path planning problem for HRMs. Especially in narrow environments, appropriate informed guiding speeds enable E-RRT* to outperform other methods.