Grasping force based manipulation for multifingered hand-arm Robot using neural networks

Abstract

Multifingered hand-arm robots play an important role in dynamic manipulation tasks. They can grasp and move various shaped objects. It is important to plan the motion of the arm and appropriately control the grasping forces for the multifingered hand-arm robots. In this paper, we perform the grasping force based manipulation of the multifingered hand-arm robot by using neural networks. The motion parameters are analyzed and planned with the constraint of the multi-arms kinematics. The optimal grasping force problem is recast as the second-order cone program. The semismooth Newton method with the Fischer-Burmeister function is then used to efficiently solve the second-order cone program. The neural network manipulation system is obtained via the fitting of the data that are generated from the optimal manipulation simulations. The simulations of optimal grasping manipulation are performed to demonstrate the effectiveness of the proposed approach.