A neural network-based methodology for inverse kinematics of a multi-finger robotic hand for gripping

Abstract

Robotic grasping and manipulation require controlling the gripper movement through different points in its work volume, necessitating inverse kinematics computations to determine joint angles. In the present work, a novel methodology, based on a radial basis function neural network, has been proposed for the inverse kinematics solution and a genetic algorithm-based approach for optimising the neural network parameters. Instead of taking the entire work volume of the hand for neural network training, a subspace of points is created in close vicinity of the given destination point. The joint variables corresponding to a destination point are obtained using a random walk algorithm that uses the forward kinematics model of the hand. Then, the subspace of points and the corresponding joint variables obtained above are used to train the neural network. This approach can provide an approximate yet fairly quick and effective solution to the inverse kinematics problem of multi-finger robot hands.