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Real-time adaptive vision servoing of a robotic manipulator

To make robotic manipulators function intelligently in an application such as flexible manufacturing, sensory feedback from an unknown environment is needed. Visual feedback represents a typical sensing system in which camera images provide feedback information, for instance, in grasping a moving object. Because image processing is time consuming, information about target position can not be obtained instantaneously for the controller. Because of the inherent time delay, the present and future position of the object has to be predicted in real-time. Since the dynamics of the objects are assumed to be unknown, the prediction will be accomplished by means of an auto-regressive discrete-time model. The predicted values and current end-effector position determine the desired trajectory point (subgoal) for the motion. The planner adapts on-line to changes in the target position. The desired trajectory is tracked by the end-effector controller. After grasping the object, problems may arise in controlling the motion of the manipulator due to the mass of the object attached to the gripper. An adaptive controller is proposed to deal with load uncertainty in the object. A simulation program is presented which demonstrates the task of grasping a moving object by a manipulator using visual feedback.