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

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 wellknown 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 consideed-forward back-propagation neural