@inproceedings{dfcf6c47a24244d2ac0cdf47c49863b3,
title = "Manipulator Path Planning based on RRT-Connect with fused Artificial Potential Field",
abstract = "In this paper, a fast planning algorithm based on the Rapidly-exploring Random Trees (RRT) algorithm is proposed to plan the efficient and obstruction-free path for manipulators. Firstly, the algorithm utilises the target bias method to reduce blind search. An adaptive probability method is also employed to adjust the algorithm's preferences according to the environment. Secondly, the efficiency is improved through the use of a dual-tree search algorithm. Additionally, the algorithm incorporates the Artificial Potential Field (APF) method, which analyses the forces acting on the path tree's points closest to the target point. The identified points are incorporated as a random point generation option in the RRT algorithm, enhancing the map's perception. Finally, it is demonstrated through simulations and experiments that the proposed algorithm reduces the search time and the total number of sampling points by 80 % compared to the traditional RRT algorithm. Compared with the existing algorithm (Fast-RRT), the search time is reduced by 20 % and the sampling points are reduced by 30 %.",
keywords = "Manipulator, Obstacle Avoidance, Path Planning, RRT",
author = "Zihan Lei and Guancheng Li and Yu Liu and Jinhui Zhang and Zhongjing Ma and Suli Zou",
note = "Publisher Copyright: {\textcopyright} 2024 Technical Committee on Control Theory, Chinese Association of Automation.; 43rd Chinese Control Conference, CCC 2024 ; Conference date: 28-07-2024 Through 31-07-2024",
year = "2024",
doi = "10.23919/CCC63176.2024.10662240",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "4687--4692",
editor = "Jing Na and Jian Sun",
booktitle = "Proceedings of the 43rd Chinese Control Conference, CCC 2024",
address = "United States",
}