Abstract:

Aiming at the problems of multiple peg-in-hole riveting parts in industrial production due to the large number of rivets, small gap between rivets and rivet holes, and irregular rivet distribution, resulting in complex assembly process constraints, high assembly accuracy requirements, and difficulty in realizing intelligent riveting process to improve assembly efficiency, a visual servo accurate assembly method of riveting parts based on adaptive extended Kalman filter is proposed. In order to realize the high-precision positioning of riveted parts assembly, on the basis of the traditional extended Kalman filtering, an adaptive noise estimator is introduced to eliminate the influence of system noise in unknown environment on the estimation accuracy of the image Jacobian matrix, and ensure the high-precision estimation of the image Jacobian matrix in the process of visual servo. In order to ensure the smooth and stable visual servo motion trajectory during rivet assembly, a sliding mode controller is designed to track the trajectory of the riveted parts, and the least squares method is introduced to estimate the image feature depth information of the riveting parts in real time online, so as to realize the high-precision assembly of the rivets. A simulation model is established with a 6-degree-of-freedom robot, and the results show that the center point feature of four rivets is selected as the control input in the irregularly distributed rivets, and the high-precision multiple peg-in-hole assembly of the rivets can be completed through the designed visual servo controller, which improves the intelligence level of the key processes in the riveting process.

Key words: riveting, multiple peg-in-hole assembly, adaptive extended Kalman filtering, in-depth online estimation, sliding mode control