Adaptive Image-Based Trajectory Tracking Control of Wheeled Mobile Robots With an Uncalibrated Fixed Camera

In this paper, the uncalibrated image-based trajectory tracking control problem of wheeled mobile robots will be studied. The motion of the wheeled mobile robot can be observed using an uncalibrated fixed camera on the ceiling. Different from traditional vision-based control strategies of wheeled mobile robots in the fixed camera configuration, the camera image plane is not required to be parallel to the motion plane of the wheeled mobile robots and the camera can be placed at a general position. To guarantee that the wheeled mobile robot can efficiently track its desired trajectory, which is specified by the desired image trajectory of a feature point at the forward axis of the wheeled mobile robot, we will propose a new adaptive image-based trajectory tracking control approach without the exact knowledge of the camera intrinsic and extrinsic parameters and the position parameter of the feature point. To eliminate the nonlinear dependence on the unknown parameters from the closed-loop system, a depth-independent image Jacobian matrix framework for the wheeled mobile robots will be developed such that unknown parameters in the closed-loop system can be linearly parameterized. In this way, adaptive laws can be designed to estimate the unknown parameters online, and the depth information of the feature point can be allowed to be time varying in this case. The Lyapunov stability analysis will also be performed to show asymptotical convergence of image position and velocity tracking errors of the wheeled mobile robot. The simulation results based on a two-wheeled mobile robot will be given in this paper to illustrate the performance of the proposed approach as well. The experimental results based on a real wheeled mobile robot will also be provided to validate the proposed approach.

I. Introduction

Visual servoing is an important technique that uses visual information for the feedback control of robots, and since 1990s, it has been an active area of research. Till now, researchers have proposed lots of visual servoing approaches, which can be mainly classified into position-based visual servoing (PBVS) and image-based visual servoing (IBVS). In PBVS approach, both of the control objective and the control law design are performed in the 3-D Cartesian space, while in IBVS approach, both of them are given in the 2-D image space. To exploit the respective advantages of PBVS and IBVS approaches, while avoiding their shortcomings, a hybrid visual servoing scheme was first proposed in [1]. As we know, this hybrid approach is also called 2.5-D visual servoing in the literature, and its control objective and control law design are implemented both in the 3-D Cartesian space and in the 2-D image space. To remove dependence on the exact knowledge of object models, homography calculation and decomposition is carried out to obtain the 3-D information used by the 2.5-D visual servoing scheme.