Yuanshan Lin, Di Wu, Xin Wang, Xiukun Wang, and Shunde Gao
Path planning, rapidly exploring random tree (RRT), optimal path, sampling-based algorithm, sampling strategy
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.
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