Original scientific paper
https://doi.org/10.1080/00051144.2023.2166451
External force estimation for industrial robots using configuration optimization
Yan Lu
; School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
Yichao Shen
; School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
Chungang Zhuang
; School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
Abstract
External force estimation for industrial robots can be applied to the scenes such as human–robot interaction and robot machining. The model-based methods have gained the attention of many researchers because they only need motor signals. However, the performance of the above-mentioned methods depends on the accuracy of robot inverse dynamic model (IDM). Traditional methods focus on improving the accuracy of IDM to obtain a better estimation performance. However, suppressing the negative impact of modelling error can be achieved without reducing the modelling error. Thus, this article proposes an external force estimation method that uses the semi-parametric friction model and applies configuration optimization based on Jacobian condition number (JCN) to reduce the modelling error and its negative impact. First, the IDM is identified. Second, the semi-parametric friction model refines the traditional model in the torque observer to improve the estimation accuracy. Third, the JCN is used as an evaluating indicator to optimize the robot configurations. Finally, several simulations and experiments on a 6-DoF industrial robot demonstrate the validity of the proposed method. This method can enhance the performance of online force estimation by up to 48.28%. In addition, the application of the proposed method is verified on the laboratory-developed machining platform.
Keywords
External force estimation; configuration optimization; semi-parametric friction model; Jacobian condition number; industrial robots
Hrčak ID:
315756
URI
Publication date:
12.1.2023.
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