Abstract
The implementation of an artificial-neural-network (ANN)-based power grasp controller is discussed. Multiple points of contact between the grasped object and finger surfaces characterize power grasps. However, modeling is especially difficult because of the nature of the contacts and the resulting closed kinematic structure. Linear programming was used to train an ANN to control the force distribution for objects using a model of the DIGITS grasping system. Force control is implemented to insure that the maximum normal force applied to the object at the contacts is set to a prespecified level whenever possible. The ANN was able to learn the appropriate nonlinear mapping between the object size and force levels to an acceptable level of accuracy and can be used as a constant-time power grasp controller. >
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