In the protective glass manufacturing industry for cell phones, placing glass pieces into the slots of the grinder requires submillimeter accuracy which only can be achieved by human workers, leading to a bottle neck in the production line. To address such issue, industrial robot equipped with vision sensors is proposed to support human workers. The high placing performance is achieved by a two step approach. In the first step, an eye-to-hand camera is installed to detect the glass piece and slot with robust vision, which can put the glass piece close to the slot and ensures a primary precision. In the second step, a closed-loop controller based on visual servo is adopted to guide the glass piece into the slot with dual eye-in-hand cameras. However, vision sensor suffers from a very low frame rate and slow image processing speed resulting in a very slow placing performance. In addition, the placing performance is substantially limited by the system parameter uncertainty. To compensate for these limitations, a dual-rate unscented Kalman filter (UKF) with dual-estimation is adopted for sensor data filtering and online parameter identification without requiring any linear parameterization of the model. Experimental results are presented to confirm the effectiveness of the proposed approach.
Visual Servo System Based on Cubature Kalman Filter and Backpropagation Neural Network
