Stochastic predictive control of robot tracking systems with dynamic visual feedback

A vision-guided robot workstation is presented which picks up workpieces from a fast-moving conveyor belt. The role of computer vision as the feedback transducer strongly affects the closed-loop dynamics of the overall system, and a tracking controller with dynamic visual feedback is designed for achieving fast response and high control accuracy. In view of the long time delay and the heavy noise corruption embedded in visual data, the problem of visual controller design is posed in the framework of stochastic optimal control theory. The Kalman filter is chosen to estimate the state of the target motion and formulated as a joint detection and adaptive estimation method. The generalized predictive control strategy is utilized to compute the optimal path control data and implemented in a weighted version. Experimental results are given to show the effectiveness of the approach