High-Performance Object Tracking and Fixation With an Online Neural Estimator

Abstract

Vision-based target tracking and fixation to keep objects that move in three dimensions in view is important for many tasks in several fields including intelligent transportation systems and robotics. Much of the visual control literature has focused on the kinematics of visual control and ignored a number of significant dynamic control issues that limit performance. In line with this, this paper presents a neural network (NN)-based binocular tracking scheme for high-performance target tracking and fixation with minimum sensory information. The procedure allows the designer to take into account the physical (Lagrangian dynamics) properties of the vision system in the control law. The design objective is to synthesize a binocular tracking controller that explicitly takes the systems dynamics into account, yet needs no knowledge of dynamic nonlinearities and joint velocity sensory information. The combined neurocontroller–observer scheme can guarantee the uniform ultimate bounds of the tracking, observer, and NN weight estimation errors under fairly general conditions on the controller–observer gains. The controller is tested and verified via simulation tests in the presence of severe target motion changes