A Path Planning Algorithm for Space Manipulator Based on Q-Learning

被引：0

作者：

Li, Taiguo ^{[1
]}

Li, Quanhong ^{[2
]}

Li, Wenxi ^{[1
]}

Xia, Jiagao ^{[1
]}

Tang, Wenhua ^{[1
]}

Wang, Weiwen ^{[1
]}

机构：

[1] Lanzhou Inst Phys, Lanzhou, Gansu, Peoples R China

[2] Gansu Agr Univ, Coll Resources & Environm, Lanzhou, Gansu, Peoples R China

来源：

PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019) | 2019年

关键词：

Space Manipulato; Grid Model; Q-Learning; Reinforcement Learning; Path Planning;

D O I：

10.1109/itaic.2019.8785427

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

an improved Q-Learning autonomous learning algorithm is proposed to solve the problem of the adaptive path planning of the space manipulator in the unknown environment. After simplification of the manipulator and obstacle model, the grid model of the environment is established, and the position of the manipulator and obstacles are randomly deployed in the grid map. Based on the analysis of the basic principle of reinforcement learning and the state generalization method, the improved Q-Learning algorithm is used to carry out the path planning. In this algorithm, the reward and punishment strategies in the path planning of the manipulator are designed, and the approximate greedy and continuous micro Botlzmann distribution behavior selection strategy is adopted. According to the autonomous learning of Q-table, the manipulator can guide its follow-up action selection and path planning, reduce the number of manipulator movement, and reduce the blindness of the learning process. The results show that the algorithm has the advantages of simple calculation, strong self-learning ability, and can successfully complete the adaptive path planning in unknown environment.

引用

页码：1566 / 1571

页数：6