Intelligent hierarchical compensation method for industrial robot positioning error based on compound branch neural network automatic creation

Jian Zhou, Lianyu Zheng, Wei Fan () and Yansheng Cao
Additional contact information
Jian Zhou: Beihang University
Lianyu Zheng: Beihang University
Wei Fan: Beihang University
Yansheng Cao: Beihang University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 4, No 35, 2915-2938

Abstract: Abstract Absolute positioning accuracy is a crucial index for evaluating industrial robot performance and the foundation for motion trajectory and machining accuracy. Current positioning error compensation methods focus on achieving unified compensation within a robot’s workspace. These methods rely heavily on expert knowledge and require a significant amount of manual intervention. To realize refined error compensation and improve the autonomy and intelligence degree of a robot, an intelligent hierarchical positioning error compensation method based on a master–slave controller is proposed in this paper. Specifically, positioning error compensation is addressed through two research questions related to positioning error level diagnosis and compensated pose prediction, and the approach consists of two major processes: automatic creation of a compound branch compensation network and hierarchical positioning error compensation. For the first process, the master controller independently grades the positioning error levels and directs the diagnosis slave controller to create a positioning error level diagnosis model in terms of the robot pose error data. Then, it directs the prediction slave controller to create several compensated pose prediction models based on the pose data of different error levels. Subsequently, the diagnosis and prediction models are integrated to form a compound branch compensation network. For the second process, the master controller first activates the diagnosis branch of the compound branch compensation network to determine the positioning error level of the current robot pose. Then, it activates the prediction branch corresponding to the determined error level to generate the compensated pose. Finally, it uses the diagnosed error level to filter the compensated pose. Experimental cases of a Stäubli robot and a UR robot are applied to verify the feasibility and effectiveness of the proposed method. The experimental results show that the proposed method reduces the positioning error of the Stäubli robot from 0.848 to 0.135 mm and the UR robot from 2.11 to 0.158 mm, outperforming relevant current methods.

Keywords: Absolute positioning accuracy; Intelligent hierarchical compensation; Compound branch neural network; Master–slave controller; Neural network automatic creation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02381-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02381-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02381-8

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().