Robotics system optimal task control (neuro-inverse kinematics approach)

A fast and efficient method for computing optimal grasping and manipulation forces is presented based on a Quadratic Optimisation formulation for a hand robotics system, where computation has been based on using the non-linear factual model of contacts. Furthermore, in order to achieve grasping while in motion, the Hand Inverse Jacobian has to be intensively computed, consequently, we investigate an efficient approach of employing an Artificial Neural Network for the multi-finger robot hand in which the object motion is defined in. The approach followed here is to let an ANN to learn the nonlinear Inverse Kinematics functional relating the hand joints positions and displacements to object displacement.