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论文类型：期刊论文

发表时间：2013-01-01

发表刊物：INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION

收录刊物：SCIE、EI、Scopus

卷号：28

期号：2

页面范围：146-153

ISSN号：0826-8185

关键字：Path planning; rapidly exploring random tree (RRT); optimal path; sampling-based algorithm; sampling strategy

摘要：This paper presents a novel efficient path planning approach denoted as RRT-Connect++ for high dimension problems with differential constraints. This work focuses on obtaining sub-optimal path within short time, while most conventional approaches strive to quickly find a feasible path or improve the quality of a path at the cost of expensive planning time. The fundamental idea of this approach is to utilize prior information to guide the search. Three modifications on the original RRT-Connect algorithm are made: constructing sampling pools with those promising vertices of trees and picking random state from them; avoiding sampling from the explored regions; adding the middle vertices during the connection operation and testing regression of vertices to guarantee the quality of trees. The performance is compared with those of several other RRT-based algorithms with three experiments to demonstrate the quality of path returned by it and its planning time efficiency. Results from the three simulation experiments show that the RRT-Connect++ can quickly find higher quality path and its efficiency is higher as well.