Car-like robot motion planning based on grey relational pattern analysis

作     者：Yeh, MF Lu, HC 

作者机构：Tatung Univ Dept Elect Engn Taipei 104 Taiwan Lunghwa Univ Sci & Technol Dept Elect Engn Taoyuan 333 Taiwan 

出 版 物：《JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS》 (J Chin Inst Eng Trans Chin Inst Eng Ser A)

年 卷 期：2003年第26卷第1期

页      面：1-12页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

基　　金：National Science Council  Republic of China  (NSC 90-2213-E-036-010) 

主　　题：car-like robot motion planning grey relational pattern analysis nonlinear programming problem convex obstacle 

摘      要：This paper proposes a simple exploring approach for a car-like robot to determine a near-optimal path between prescribed initial and goal positions, based on the nonlinear programming problem and grey relational pattern analysis. No matter whether the considered workspace is known or not in advance, the proposed approach can make the car-like robot explore and move in a workspace containing multiple convex obstacles. Unlike other exploring approaches, the proposed find-path procedure must consist of at least one trial. Each trial contains two main stages, one is termed the forward search stage and the other is named the backward search stage. After all possible trials have occurred, an additional stage, called the decision-making stage, is then introduced to determine the near-optimal and collision-free path, which is guaranteed to reach the goal. In addition, the presented method is applicable for on-line planning applications and, furthermore, can solve the so-called local minimum problems. Simulation results for a workspace with multiple convex obstacles demonstrate the searching performance of our approach and its potential as an on-line path planner in a known or an unknown workspace.