Optimization Method for Configuration Set for Field Calibration of Industrial Robot

Abstract

In the calibration process of industrial robots, selecting the optimal measurement position configuration can improve calibration accuracy. However, existing research on the optimal measurement configurations selection mainly focuses on open-loop measurement methods using laser trackers. The exploration of the optimal measurement configuration selection for closed-loop measurement methods has not been developed to a large extent. This research introduces a measurement configurations selection observability index based on the position difference error equation, as well as an optimization method based on this index. Initially, we establish an error model for the kinematic parameters of industrial robots, which is based on the position difference of measurement configurations. Subsequently, by minimizing the relative error of the measurement, we proposed an observability index, denoted as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{O}_{\boldsymbol{e}}$</tex-math></inline-formula>. Using this index, construct a fitness function and use particle swarm optimization (PSO) algorithm to optimize the selection of robot measurement configuration set. The effectiveness of the proposed optimization method has been demonstrated through experiments conducted on CRP RA07 and JAKA Zu seven robots. The experimental results show that using the observability index <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{O}_{\boldsymbol{e}}$</tex-math></inline-formula> to select the optimal configuration set can improve the calibration accuracy of the robot by 25.8% and 37.0%, respectively. The verification experiment using a laser tracker confirms that our optimal configuration set selection method can effectively improve calibration accuracy. This research fills the gap in the selection of optimal measurement configurations for constrained measurement. It is of great significance for field calibration research of robots.