Efficient path planning for high-DOF articulated robots with adaptive dimensionality

Abstract

This paper proposes a method for path planning of high-degree of freedom (DOF) articulated robots with adaptive dimensionality. For an efficient path planning in high-dimensional C-space (configuration space), first we describe an adaptive body selection that selects the robot bodies and joints depending on the complexity of path planning. It means that the robot may use necessary DOF to achieve a path planning task. The adaptive body selection method builds the C-space with adaptive dimensionality for a sampling-based path planner. Next, by using the adaptive body selection, the adaptive Rapidly-Exploring Random Tree (RRT) algorithm is introduced, which incrementally grows RRTs in the adaptive dimensional C-space. And we show through several simulation results that the proposed method is more efficient than the original RRT-based path planner, which requires full-dimensional planning.