In order to construct an intelligent robot system, the implementation of sensor systems is a prime requirement. With the increasing demands for hand-eye coordinating systems, problems of how to design, construct, and utilize hand-eye systems effectively have been realized which have to be solved. For example, teaching tasks to a hand-eye system requires off-line programming of the visual sensors as well as the manipulators. Although much attention has been paid recently to robot simulators for assisting the off-line programming of manipulators, less work has been done on simulators of the sensors. We have developed a hand-eye action simulator system called HEAVEN. This system provides model-based functions to assist the hand-eye system in visual recognition and monitoring the robot environment. This paper describes a function of assisting cameras in occlusion avoidance to input adequate image data without occlusion. The problem to select the best viewpoint for a camera is defined as to evaluate the viewpoints on a geodesic dome generated around a target object model. Occlusion-free space is obtained as regions on the geodesic dome by using a depth buffer algorithm. Then distance transformation of the occlusion-free regions gives candidates of the best occlusion-free viewpoint. Experimental results using a camera-in-hand system demonstrate the usefulness of the HEAVEN system in planning occlusion avoidance.
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