Uncalibrated eye-in-hand visual servoing

In this paper we present new uncalibrated control schemes for vision-guided robotic trucking of a moving target using a moving camera. These control methods are applied to an uncalibrated robotic system with eye-in-hand visual feedback. Without a priori knowledge of the robot's kinematic model or camera calibration, the system is able to track a moving object through a variety of motions and maintain the object's image features in a desired position in the image plane. These control schemes estimate the system Jacobian as well as changes in target features due to target motion. Four novel strategies are simulated and a variety of parameters are investigated with respect to performance. Simulation results suggest that a Gauss-Newton method utilizing a partitioned Broyden's method for model estimation provides the best steady-state tracking behavior.