Desired orientation RRT (DO-RRT) for autonomous vehicle in narrow cluttered spaces
Seho Shin, Joonwoo Ahn, Jaeheung Park
Abstract
Autonomous vehicles are actively being developed from ADAS(Advanced Driver Assistance Systems) toward fully autonomous vehicles. Motion planning is one of the most important key technologies for fully autonomous vehicles, especially when they are operated in constrained narrow space such as parking lot. In this the motion planning is challenging because it requires many changes in forward and reverse directions and adjustments of position and orientation. In this paper, an efficient motion planning algorithm is proposed based on Rapidly-exploring Random Trees (RRT) by specifying desired orientation during the tree expansion. A tangential vector space for desired orientation is used to model nonholonomic constraints of a vehicle and geometric constraints of obstacles. The proposed algorithm has been tested on various situations and its results demonstrated much faster performance compared to a nonholonomic RRT algorithm.
BibTeX
@inproceedings{iros2016_desiredorientati,
title = {Desired orientation RRT (DO-RRT) for autonomous vehicle in narrow cluttered spaces},
author = {Seho Shin and Joonwoo Ahn and Jaeheung Park},
booktitle = {IROS 2016},
year = {2016}
}