Robot kinematic control based on bidirectional mapping neural network

Abstract

The authors present a novel method of accomplishing robot kinematic control based on a bidirectional mapping neural network (BMNN). The BMNN constructed is composed of a multilayer feedforward network with hidden units having sinusoidal activation functions and a feedback network forming a recurrent loop around the feedforward network. The feedforward network can be trained to accurately represent the forward kinematic equations of a robot arm. The feedback network iteratively generates joint-angle updates based on a Lyapunov function to modify the current joint angles in such a way that the output of the forward network converges to the desired Cartesian position and orientation. The proposed BMNN offers the following advantages over the conventional approaches: (1) the accurate computation of robot forward and inverse kinematic solutions with simple training; (2) the ability to handle one-too-many inverse mapping required for redundant arm kinematics solutions; and (3) the automatic generation of arm trajectories. Simulation results are shown