A predictive model for narrow passage path planner by using Support Vector Machine in changing environments

Abstract

Narrow passages in changing environments create huge difficulties, since locations and shapes of narrow passages in Configuration Space(C-space) change frequently. It is very important for a planner to identify narrow passages in real time and boost valid points within them effectively. A novel narrow passage predictive model for designing a path planner in changing environments is proposed in this paper. Firstly, an Expanded Dynamic Bridge Builder is presented to identify narrow passages rapidly with validity-toggle sampling points in C-space. Secondly, the predictive model is adopted to sample possibly free points within these narrow passages without invoking any collision detection in order to avoid intense computational complexity. The predictive model is obtained by the famous classification method of Support Vector Machine (SVM). A new feature, which includes a group of points' distance and validity information, is proposed in SVM training process to capture approximate structure of local narrow passages. Therefore, the predictive model can excavate the hidden similar structure of local narrow passages. Experiments carried out with two 6-DOFs manipulators show that our approach gain higher success rate of planning and time efficiency than other related methods.