Reoriented Short-Cuts (RSC): An Adjustment Method for Locally Optimal Path Short-Cutting in High DoF Configuration Spaces

摘要

This paper presents Reoriented Short-Cuts (RSC): A modification of the traditional Short-Cut technique, allowing almost sure, single homotopy class, asymptotic convergence in high degree of freedom (DoF) problems. An additional Informed Gaussian Sampling (IGS) technique is also introduced for convergence comparison. Traditionally, Short-Cut methods are used as a final technique to further optimize an initially found path. Typical Short-Cut methods fail as a single DoF may converge faster than the remaining, creating a zero-volume region between path segments and objects, halting further improvements. Previous attempts to solve this separate DoFs individually, drastically increasing collision checking computation. RSC and IGS control the shifting of the vertex to be Short-Cut, moving vertex positions by reorienting the line segments, removing the zero-volume convergence region. These methods are compared to similar strategies in a variety of problems including random worlds, and robot manipulation, to show the convergence across both translation and rotation oriented problems.