Neural Network Control of Force Distribution for Power Grasp

The implementation of an Artiicial Neural Network (ANN) based power grasp controller is discussed. Multiple points of contact between the grasped object and nger surfaces characterize power grasps and result in highly stable grasps. However, modeling is especially diicult 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 pre-speciied 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.