Haitao Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Haitao Liu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jiangbo Lan
School of Large Aircraft Engineering, Beihang University, Beijing 100191, China
1 shared publication - Qi Qi
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
1 shared publication - Shuo Chen
School of Large Aircraft Engineering, Beihang University, Beijing 100191, China
1 shared publication - Wenlei Xiao
School of Mechanical Engineering & Automation, Beihang University, Beijing, China
1 shared publication - Yong Tao
School of Mechanical Engineering and Automation, Beihang University, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 2 September 2023
- Published by MDPI AG in Electronics
- Vol. 12 (17) , 3718
An Off-Line Error Compensation Method for Absolute Positioning Accuracy of Industrial Robots Based on Differential Evolution and Deep Belief Networks
Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is proposed based on deep belief networks using an off-line compensation method. A differential evolution algorithm is presented to optimize the networks. Combined with the evidence theory, a position error mapping model is proposed to realize the absolute positioning accuracy compensation of industrial robots. Experiments were conducted using a laser tracker AT901-B on an industrial robot KR6_R700 sixx_CR. The absolute position error of the end of the robot was reduced from 0.469 mm to 0.084 mm, improving the accuracy by 82.14% after the compensation. Experimental results demonstrated that the proposed compensation algorithm could improve the absolute positioning accuracy of industrial robots, as well as its potential uses for precise operational tasks.
- 2 September 2023
- Published by MDPI AG in Electronics
- Vol. 12 (17) , 3718
Open AccessAn Off-Line Error Compensation Method for Absolute Positioning Accuracy of Industrial Robots Based on Differential Evolution and Deep Belief Networks
Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is proposed based on deep belief networks using an off-line compensation method. A differential evolution algorithm is presented to optimize the networks. Combined with the evidence theory, a position error mapping model is proposed to realize the absolute positioning accuracy compensation of industrial robots. Experiments were conducted using a laser tracker AT901-B on an industrial robot KR6_R700 sixx_CR. The absolute position error of the end of the robot was reduced from 0.469 mm to 0.084 mm, improving the accuracy by 82.14% after the compensation. Experimental results demonstrated that the proposed compensation algorithm could improve the absolute positioning accuracy of industrial robots, as well as its potential uses for precise operational tasks.