Adaptive self-calibration of vision-based robot systems

An adaptive self-learning process to dynamically and continuously learn the transformation between the camera space and the robot space is discussed. The process is referred to as the adaptive self-calibration of hand-eye systems in which a visual-feedback-based self-learning process is used for dynamically and continuously learning the hand-eye transformation through repetitive operation trials. The hand-eye system calibration is used in situ and in real time while the system is operating. Recursive real-time implementation using adaptive and square-root Kalman filtering techniques is described and recent related research is reviewed. An experimental stereo-vision-based hand-eye system is described. Both simulation and experimental results are presented