Manipulation of unmodeled objects using intelligent grasping schemes

We investigate "intelligent' grasping schemes using a fuzzy logic rule base expert system. In this paper, we use a vision system, robot arm and mechanical hand to locate and manipulate unmodeled, randomly placed objects of various sizes and shapes. In the pregrasp stage, we use vision data to provide a nonlinear mapping from object characteristics to hand configuration. In the postgrasp stage, we use hand data to ascertain the security of the grasp. Computational geometry is used to gauge the quality of them grasp and to quantify and validate the choice of hand configurations generated by the fuzzy logic expert system. The system is Implemented within a low-cost virtual collaborative environment.