A Lagrangian network for multifingered hand grasping force optimization

Abstract

A Lagrangian network which is developed from the Lagrange multiplier method, is proposed for multifingered hand grasping force optimization. The Lagrangian network is a recurrent neural network and is shown to be capable of taking into account the nonlinearity of the friction constraints between contacts. By giving the external load and the finger joint torque limits to the neural network, it asymptotically converges to a set of optimal grasping forces. Simulation results show that the proposed approach gives a better quality of optimal grasping force compared to other approaches in the literature.