Servo control method of picking manipulator based on adaptive traceless Kalman filter

Abstract: Image Jacobian matrix is core portion of robot vision servo system. During estimating image Jacobian matrix with traditional methodology of KF (Kalman Filter) and UKF (Unscented Kalman Filter), the system noises of unknown statistical properties is determined according to priori knowledge, which is remained unchanged in system. However, this methodology of dealing system noises would make image Jacobian matrix’s estimation inaccuracy because of time-varying system noises. Based on traditional methodology of KF and UKF, in this paper, we structured propagator of system noises estimation with the difference value of system state predicted value and updated value, and raised Adoption Unscented Kalman Filter to estimate image Jacobian matrix. Two points on the tomato string’s stem, clamping point and picking point, were chosen as feature points in the image feature space. The center of mass of fruit stem was extracted as the holding point. According to the vertical growth characteristics of tomato clusters and the distance between the holding point on the end and the cutting point on the image, a point above the holding point was extracted as the cutting point. Image Jacobian matrix was used to transform information of pixel difference in image feature space to change in pose of end-effector in manipulator motion space. First, results of image feature space localization experiment showed that six image feature errors were smaller and the average image feature error of visual servo system based on AUKF was 4.978 pixes/mm in condition of unknown system noises. Compared with visual servo system based on KF and UKF, average image feature error reduced 54.44% and 24.78%, respectively. Second, three dimensional space positioning experiment between end-effector and target picking point showed that the final distance between end-effector and target picking point was 4.19 mm, reduced by 79.74% and 80.38% compared to methods of KF and UKF. Third, the response time of visual servo control system based on KF method, UKF method and AUKF method to estimate image Jacobian matrix was 2, 1.5 and 1.2 s, respectively. The picking experiment was carried out on the visual servo test platform of the seven-degree-of-freedom picking manipulator. The visual system based on each visual servo control method was tested for 30 times. For the visual servo system based on AUKF method estimation, the success rate of picking the static target was 90%, and the success rate of picking the dynamic target was 83%. Compared with KF methodology and UKF methodology, the success rate of static picking test was increased by 17 and 10 percentage points, respectively, and the success rate of dynamic picking test was increased by 16 and 10 percentage points, respectively. The results showed that the static picking time based on AUKF methodology was the 18 s, compared with the visual servo system based on KF methodology and UKF methodology, the time of static picking was reduced by 35.71% and 25%, respectively. The average dynamic picking time of visual servo system based on AUKF was 22 s and the test time of dynamic picking was reduced by 35.29% and 26.67%, respectively compared with the visual servo system based on KF methodology and UKF methodology. The image Jacobian matrix based on AUKF was suitable for the dynamic picking of tomato clusters, and the accuracy of visual servo control under dynamic condition was lower than that under static condition. Test results suggested that the proposed AUKF method with KF method and the UKF method compared to estimating by image Jacobin matrix, and AUKF method to estimate the image Jacobin matrix, reduced the process of picking robot visual servo control system noise interference, which made the picking robot visual servo control system of processing noise adapted to the change of the visual servo system, resulting in higher positioning precision for picking robot visual servo control system .