A low-computation object grasping method by using primitive shapes and in-hand proximity sensing

We report on a novel low-computation object grasping method that can classify complex objects into primitive shapes and then select the object grasping posture based on predefined grasping postures associated with the approximated primitive shapes. In this approach, the object is not precisely modeled, and the grasping posture is selected from a small number of candidates without massive search; thus, the grasping posture for the object can be quickly derived. Because the object and primitive shape have geometrical discrepancy, the gripper is compliant and equipped with infrared proximity sensors on the fingers to compensate for the geometrical uncertainties and provide adequate contact between the object and the grippers. The methodology is experimentally evaluated with several types of objects in different postures.