Model Predictive Control for Uncalibrated and Constrained Image-Based Visual Servoing Without Joint Velocity Measurements

Abstract

This paper presents a novel scheme for image-based visual servoing (IBVS) of a robot manipulator by considering robot dynamics without using joint velocity measurements in the presence of constraints, uncalibrated camera intrinsic and extrinsic parameters and unknown feature position parameters. An approach to design model predictive control (MPC) method based on identification algorithm and sliding mode observer has been proposed. Based on the MPC method, the IBVS tasks can be considered as a nonlinear optimization problem while the constraints due to the visibility constraint and the torque constraint can be explicitly taken into account. By using the depth-independent interaction matrix framework, the identification algorithm can be used to update the unknown parameters and the prediction model. In addition, many existing controllers require the joint velocity measurements which can be contaminated by noises, thus resulting in the IBVS performance degradation. To overcome the problem without joint velocity measurements, the sliding mode observer is designed to estimate the joint velocities of the IBVS system. The simulation results for both eye-in-hand and eye-to-hand camera configurations are presented to verify the effectiveness of the proposed control method. INDEX TERMS Image-based visual servoing, model predictive control, constrained optimization control, depth-independent interaction matrix, sliding mode observer.

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

“…Here a potential field force θ (t) is introduced to keep theM (t) full rank. The negative gradient function ∂ θ (t) ∂θ (t) is used to update theθ (t) to keep it away from the singularity region ofM (t) It also guarantees the existence ofD + (t), referred to [31] for details. Next, we define the estimation error of the projection matrix, that is M (t) − M (t) .…”

“…The depth-independent interaction matrix framework and the depth parameter adaptive laws are developed to compensate the unknown depth. In [31], the visual tracking tasks are considered as a nonlinear optimization problem. The depth-independent interaction matrix is used for the linearly extraction of unknown camera model parameters, the model predictive control (MPC) is used to estimate parameters.…”

“…The depth-independent interaction matrix is used for the linearly extraction of unknown camera model parameters, the model predictive control (MPC) is used to estimate parameters. Furthermore, on the basis of [31], [32] introduces visual constraints to the predictive control methods for avoid the visual features outside the camera FOV.…”

“…The advantage of this method is convenient for applying to a real robot platform. For the sake of the design of visual tracking controller, based on the depth-independent interaction matrix in [30] and [31], we consider to design the parameter adaptive law including projection error compensation to further improve the estimation accuracy of camera model parameters.…”

Integral-Sliding-Mode-Observer-Based Structure and Motion Estimation of a Single Object in General Motion Using a Monocular Dynamic Camera

“…Accordingly, the structure and motion (SaM) estimation problem has been extensively studied in the field of vision-based control [31], [32]. Structure and/or motion estimation can be readily employed for the visual servoing and tracking control of robots based on a vision system [33]- [36]. In particular, in the research on Euclidean 3D reconstruction using a monocular camera for different camera and object motions [12], [19], [37]- [40], [42]- [49], either the range and object velocity were not simultaneously estimated or the camera and object motions were constrained.…”