Neural networks to determine task oriented dexterity indices for an underwater vehicle-manipulator system

Abstract

A method for the fast approximation of dexterity indices for given underwater vehicle-manipulator systems (UVMS) configurations is presented. Common underwater tasks are associated with two well-known dexterity indices and two types of neural networks are designed and trained to approximate each one of them. The method avoids the lengthy calculation of the Jacobian, its determinant and the computationally expensive procedure of singular value decomposition required to compute the dexterity indices. It provides directly and in a considerably reduced computational time the selected dexterity index value for the given configuration of the system. The full kinematic model of the UVMS is considered and the NN training dataset is formulated by the conventional calculation of the selected dexterity indices. A comparison between the computational cost of the analytical calculation of the indices and their approximation by the two NN is presented for the validation of the proposed approach. This paper contributes mainly on broadening the applications of NN to a problem of high complexity and of high importance for UVMS high performance intervention.