@inproceedings{37b5d1ecc0544d41adeafc0bed4a7c42,
title = "An Intelligent Planning Method For The Multi-Rotor Manipulation Robot With Reinforcement Learning",
abstract = "In this paper, an intelligent planning method based on proximal policy optimization algorithm for the multi-rotor aerial manipulation robot is presented. This method can not only avoid the complexity of the dynamic analysis and modeling but also the large disturbance of the manipulator to the robot's body produced by the independent kinematic planning method. The disadvantage of kinematic planning method in aerial manipulation is given. The detailed structure of the adopted training and simulation environment is introduced. A deep reinforcement learning formulation is proposed to deal with the aerial manipulation of the robot. The particulars of setup in training and simulation are illustrated and the practical training and a series tests are carried out. The results of simulation proved the feasibility of this intelligent planning method and its advantages in real-time planning or replanning to enhance the stability of the robot in manipulation.",
keywords = "Aerial Manipulation, Reinforcement Learning, Robot Planning, UAV",
author = "Haoyuan Liu and Pin Guo and Xueying Jin and Huichao Deng and Kun Xu and Xilun Ding",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Conference on Mechatronics and Automation, ICMA 2022 ; Conference date: 07-08-2022 Through 10-08-2022",
year = "2022",
doi = "10.1109/ICMA54519.2022.9856385",
language = "英语",
series = "2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1028--1033",
booktitle = "2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022",
address = "美国",
}