A unified design method for adaptive visual tracking control of robots with eye-in-hand/fixed camera configuration

Liang, Xinwu; Wang, Hesheng; Liu, Yunhui; Chen, Weidong; Zhao, Jie

doi:10.1016/j.automatica.2015.06.018

Cited by 54 publications

(27 citation statements)

References 45 publications

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“…In underwater visual servoing, camera parameters, UVMS kinematic modelling, and feature position are always difficult to accurately achieve. A linear separation method to collect camera uncertainties into vectors is given in [18,21], however this method can only be used for the fixed manipulator and under the assumption that kinematics of the manipulator is perfectly known. In this section, we propose a novel linear separation method to collect these uncertainties into constant vectors without this assumption for free-floating based articulated manipulators.…”

Section: Kinematics and Dynamicsmentioning

confidence: 99%

“…For object grasping, a manipulator with an eye-in-hand camera is common, because the feature points should be extracted on the object, however, these articles only concern the situation about the eye-to-hand camera configuration. Liang et al [21] proposed a unified method for both eye-in-hand and eye-to-hand camera configurations, this work also gave a separation algorithm on kinematic properties of the manipulator system, however, the uncertain kinematics of the manipulator was not considered. Wang et al [22] proposed a dynamic uncalibrated visual servoing approach for a free-floating based manipulator, however, it assumed the system has no external disturbances, and this assumption is often hard to satisfy in underwater environment.…”

Section: Introductionmentioning

confidence: 99%

“…To solve these problems, we propose a novel uncalibrated visual servoing method for UVMSs with an eye-in-hand configuration camera. The main contributions of this work can be summarized as follows: firstly, we propose a novel linear separation method to collect some uncertainties into constant vectors compared with the one in [21,28]. This method can be applied for other free-floating based articulated manipulators.…”

Section: Introductionmentioning

confidence: 99%

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Uncalibrated Visual Servoing for Underwater Vehicle Manipulator Systems with an Eye in Hand Configuration Camera

Huang

et al. 2019

Sensors

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This paper presents an uncalibrated visual servoing scheme for underwater vehicle manipulator systems (UVMSs) with an eye-in-hand camera under uncertainties. These uncertainties contain vision sensor parameters, UVMS kinematics and feature position information. At first, a linear separation approach is addressed to collect these uncertainties into vectors, and this approach can also be utilized in other free-floating based manipulator systems. Secondly, a novel nonlinear adaptive controller is proposed to achieve image error convergence by estimating these vectors, the gradient projection method is utilized to optimize the restoring moments. Thirdly, a high order disturbance observer is addressed to deal with time-varying disturbances, and the convergence of the image errors is proved under the Lyapunov theory. Finally, in order to illustrate the effectiveness of the proposed method, numerical simulations based on a 9 degrees of freedom (DOFs) UVMS with an eye-in-hand camera are conducted. In simulations, the UVMS is expected to track a circle trajectory on the image plane, meanwhile, time-varying disturbances are exerted on the system. The proposed scheme can achieve accurate and smooth tracking results during simulations.

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Section: Kinematics and Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncalibrated Visual Servoing for Underwater Vehicle Manipulator Systems with an Eye in Hand Configuration Camera

Huang

et al. 2019

Sensors

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“…After taking the time derivative of (6), the open-loop dynamics for e r .t / can be obtained (see [27] for further details)…”

Section: Open-loop Error Systemmentioning

confidence: 99%

“…Similar to (6), denote e r .t / and e rd .t / to quantify the rotation of F relative to F (i.e., N R) and the rotation of F d relative to F (i.e., N R d ), respectively. Based on (8), (9), and the fact that the plane and the reference coordinate frame F are stationary (i.e., !…”

Section: Trajectory Tracking Extensionmentioning

confidence: 99%

Asymptotic moving object tracking with trajectory tracking extension: A homography‐based approach

Zhang

Chen

Jia

2017

Intl J Robust & Nonlinear

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Summary In this paper, a homography‐based visual servo controller is developed for a rigid body to track a moving object in three‐dimensional space with a fixed relative pose. Specifically, a monocular camera is mounted on the rigid body, and the desired relative pose is expressed by a pre‐recorded reference image. Homography is exploited to obtain the orientation and scaled position for controller design. Considering the unknown moving object's velocities and distance information, a continuous nonlinear visual controller is developed using the robust integral of the signum of the error methodology. To facilitate the stability analysis, the system uncertainties regarding the moving object's velocities and distance information are divided into the error‐unrelated system uncertainties and the error‐related system uncertainties. After that, the upper bounds of the error‐related system uncertainties are derived with composited system errors. An asymptotic tracking of the leading object is proved based on the Lyapunov methods and the derived upper bounds. In addition, the proposed controller is extended to address the trajectory tracking problem. Simulation results validate the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.

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Model Predictive Control for Constrained Image‐Based Visual Servoing in Uncalibrated Environments

Qiu

Liang

2018

Asian Journal of Control

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This paper presents a novel approach for image-based visual servoing (IBVS) of a robotic system by considering the constraints in the case when the camera intrinsic and extrinsic parameters are uncalibrated and the position parameters of the features in 3-D space are unknown. Based on the model predictive control method, the robotic system's input and output constraints, such as visibility constraints and actuators limitations, can be explicitly taken into account. Most of the constrained IBVS controllers use the traditional image Jacobian matrix, the proposed IBVS scheme is developed by using the depth-independent interaction matrix. The unknown parameters can appear linearly in the prediction model and they can be estimated by the identification algorithm effectively. In addition, the model predictive control determines the optimal control input and updates the estimated parameters together with the prediction model. The proposed approach can simultaneously handle system constraints, unknown camera parameters and depth parameters. Both the visual positioning and tracking tasks can be achieved desired performances. Simulation results based on a 2-DOF planar robot manipulator for both the eye-in-hand and eye-to-hand camera configurations are used to demonstrate the effectiveness of the proposed method.

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A unified design method for adaptive visual tracking control of robots with eye-in-hand/fixed camera configuration

Cited by 54 publications

References 45 publications

Uncalibrated Visual Servoing for Underwater Vehicle Manipulator Systems with an Eye in Hand Configuration Camera

Uncalibrated Visual Servoing for Underwater Vehicle Manipulator Systems with an Eye in Hand Configuration Camera

Asymptotic moving object tracking with trajectory tracking extension: A homography‐based approach

Model Predictive Control for Constrained Image‐Based Visual Servoing in Uncalibrated Environments

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