@inproceedings{cf02e7c98a5147178c1f838de14eb91a,
title = "Locally guided multiple Bi-RRT∗ for fast path planning in narrow passages",
abstract = "Rapidly-exploring Random Tree Star (RRT<) and its variants can provide a collision-free and asymptotic optimal solution for many path planning problems. However, it is inefficient for many RRT< based variants to rapidly find one initial solution in a clustered environment with narrow passages, since consuming high memory as well as time, due to a large number of iterations in sampling critical nodes. To overcome this problem, the paper proposes the Locally Guided Multiple Bi-RRT< (LGM-BRRT<) method, which can provide a fast solution by incorporating an improved bridge-test and a novel search strategy based on local guidance. It ensures an accelerated success rate and more efficient memory utilization compared with Bidirectional RRT<(BRRT<), and it is easy for implementation as well. It was verified on different types of scenarios in terms of efficiency and success rate. The results demonstrated that LGM-BRRT< is beneficial for fast path planning in a clustered environment with narrow passages.",
keywords = "Fast Planning, Local Guidance, Narrow Passage, Path Planning",
author = "Xin Shu and Fenglei Ni and Zhou Zhou and Yechao Liu and Hong Liu and Tian Zou",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 ; Conference date: 06-12-2019 Through 08-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ROBIO49542.2019.8961757",
language = "英语",
series = "IEEE International Conference on Robotics and Biomimetics, ROBIO 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2085--2091",
booktitle = "IEEE International Conference on Robotics and Biomimetics, ROBIO 2019",
address = "美国",
}