A Novel Path Planning Method for Articulated Road Roller Using Support Vector Machine and Longest Accessible Path With Course Correction

Abstract

Aiming at Articulated Road Roller (ARR) path planning near the construction area, a novel path planning method using Support Vector Machine (SVM) and Longest Accessible Path with Course Correction (LAP-CC) is proposed. First, the feasible path selected by Goal-Directed RRT (GDRRT) will be repeated 20 times to determine which category the obstacle belongs to, and then the error penalty factor and kernel parameter of SVM are selected by the grid search and cross-validation parameter optimization algorithm. Then, A set of different virtual obstacles are used on both sides of ARR to control the start and end points of the zero-potential decision boundary. Next, Longest Accessible Path (LAP) is presented to search critical turning points on the decision boundary. Finally, Course Correction (CC) corrects ARR course at these critical turning points. Simulation experiments show that the novel path planning method for ARR using SVM and LAP-CC has the advantages of simplicity, feasibility, low computational cost and good repeatability.

Keywords

• Support vector machine
• longest accessible path
• course correction
• path planning
• articulated road roller
• virtual obstacles