<title>Intelligent vision-based part feeding on dynamic pursuit of moving objects</title>

Abstract

The paper addresses the problem of grasping moving objects from a vibratory feeder with robotic hand-eye coordination. Since the dynamics of moving targets on the vibratory feeder are highly nonlinear and impractical to model accurately, the problem has been formulated in the context of Prey Capture with the robot as a `pursuer' and a moving object as a passive `prey.' A hierarchical vision-based intelligent controller has been developed and implemented in the Factory-of-the Future Kitting Cell at Georgia Tech. The first and second levels are based on the principle of fuzzy logic to help the robot search for an object of interest and then pursue it. The third level is based on a backpropagation neural network to predict its position at which the robot gripper grasps it. The feasibility of the concept and the control strategies was verified by two experiments. The first experiment showed that the fuzzy logic controller could command the robot to successfully follow the highly nonlinear motion of a moving object and approach its vicinity. The second experiment demonstrated that the neural network could estimate its position fairly accurately in a finite period of time after the command of grasp operations was issued.