Visual Servo Tracking of Wheeled Mobile Robots With Unknown Extrinsic Parameters

Qiu, Yu; Li, Baoquan; Shi, Wuxi; Zhang, Xuebo

doi:10.1109/tie.2019.2891407

Cited by 45 publications

(24 citation statements)

References 38 publications

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“…Therefore, the uncertainty of camera model parameters should be considered in the design of tracking controller. In this section, Image-based visual servoing method (IBVS) method [29] is adopted, where the image Jacobian matrix is linearized, which contains camera internal and external parameters, and the parameter adaptive law is designed to solve the parameter uncertainty of the camera model.…”

Section: Uncalibrated Visual Tracking Controller Designmentioning

confidence: 99%

“…whereŷ (t) denote the estimation of the image velocity trajectory, which is calculated by the estimation of projection matrixM (t). By (29), the estimation of the depthindependent Jacobian matrixD + is used to map the reference velocityẏ r (t) to the joint space of the robot arm, then the reference in the joint space is given,…”

Section: B Design Of Adaptive Law For Camera Parametersmentioning

confidence: 99%

“…In order to improve the accuracy of parameter estimation, the new estimation errors of depth z (t) and depth-independent Jacobian matrix D (t) are introduced as the compensation of projection error estimation. According to (29), the error is defined as, By the regression matrix (x (t), y (t)) and (ẏ(t), x(t),ẋ(t)), the adaptive update law of θ(t) is designed as,…”

Section: B Design Of Adaptive Law For Camera Parametersmentioning

confidence: 99%

“…In addition, inaccurate calibration of camera model parameters will reduce the performance of visual tracking. In [29], an adaptive visual servo tracking controller is designed to compensate unknown external parameters and visual feature depth when the parameters completely unknown. The camera model parameters and depth of image feature are included in the image Jacobian matrix.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

Zhang

Tang

et al. 2020

IEEE Access

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This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator. INDEX TERMS Visual tracking, robot manipulation, Kalman filter, occlusion.

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Section: Uncalibrated Visual Tracking Controller Designmentioning

confidence: 99%

Section: B Design Of Adaptive Law For Camera Parametersmentioning

confidence: 99%

Section: B Design Of Adaptive Law For Camera Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

Zhang

Tang

et al. 2020

IEEE Access

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“…In the motion control of a wheel robot based on a visual servo, the center position of the visual system is mostly the same as the center position of the robot body, but some settings that deviate from the center position of the robot are conducive to its motion, which will bring deviation to the visual system, resulting in the convergence failure of the visual error. In order to solve this problem, Qiu [31] designed a motion tracking method based on visual servo, which deviated from the center of the robot body, to solve the influence of the translation of the uncalibrated camera on the parameters of the visual system. In the visual servo control of most mobile robots, the trajectory and the desired position image must be given.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

et al. 2020

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This paper proposes a visual servo scheme for hoisting positioning under disturbance conditions. In actual hoisting work, disturbances such as equipment and load vibration are inevitable, which brings challenges to the development of a visual servo for hoisting positioning. The main problems are as follows: (1) the correlation between visual error and disturbance is not considered or well resolved; (2) the disturbance has a great influence on the control stability, but it is difficult to model. At present, there is no detailed research on the above problems. In this paper, the visual error is defined by the image error of the feedback signal based on dynamic equations containing disturbances. An adaptive sliding mode control algorithm is employed to decrease the influence of external disturbance, and the coefficient of the slide surface is established based on the adaptive gain. In view of the belief that it is difficult to model disturbance terms, a nonlinear disturbance observer is introduced to obtain equivalent disturbance. On this basis, an adaptive control algorithm with disturbance compensation is proposed to improve the robustness of the visual servo system. We use Lyapunov’s method to analyze the stability conditions of the system. Compared with the other state-of-the-art methods, the simulation results show that our method has superior performance in convergence, accuracy, and restraining disturbance. Finally, the proposed algorithm is applied to the hoisting platform for experimental research, which proves the effectiveness of the controller.

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Quasi‐min‐max MPC for visual servoing stabilization of omnidirectional wheeled mobile robots

Lin

Xing

et al. 2022

Asian Journal of Control

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This paper proposes a new visual servoing quasi‐min‐max MPC algorithm for stabilization control of an omnidirectional wheeled mobile robot subject to physical and visual constraints. The visual servoing dynamics of the robot are modeled as the state‐dependent linear error system with nonlinear control inputs of rotation and deflection velocities of wheels. The state‐dependent linear error system is covered as linear parameters‐varying models which is used to design the visual servoing quasi‐min‐max MPC controller. The actual control inputs of the robot are then computed by the solution of an inverse algebraic equation of the MPC actions. The recursive feasibility and stability of the new visual servoing MPC are ensured by some LMIs conditions. The performance and practicability of the visual servoing MPC are verified by some simulation and experiment results.

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Visual Servo Tracking of Wheeled Mobile Robots With Unknown Extrinsic Parameters

Cited by 45 publications

References 38 publications

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

Quasi‐min‐max MPC for visual servoing stabilization of omnidirectional wheeled mobile robots

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