Detection and tracking for robotic visual servoing systems

Robot manipulators require knowledge about their environment in order to perform their desired actions. In several robotic tasks, vision sensors play a critical role by providing the necessary quantity and quality of information regarding the robot's environment. For example, "visual servoing" algorithms may control a robot manipulator in order to track moving objects that are being imaged by a camera. Current visual servoing systems often lack the ability to detect automatically objects that appear within the camera's field of view. In this research, we present a robust "figure/ground" framework for visually detecting objects of interest. An important contribution of this research is a collection of optimization schemes that allow the detection framework to operate within the real-time limits of visual servoing systems. The most significant of these schemes involves the use of "spontaneous" and "continuous" domains. The number and location of continuous domains are allowed to change over time, adjusting to the dynamic conditions of the detection process. We have developed actual servoing systems in order to test the framework's feasibility and to demonstrate its usefulness for visually controlling a robot manipulator.