Convergence analysis for the uncalibrated robotic hand–eye coordination based on the unmodeled dynamics observer

Su, Jianbo

doi:10.1017/s0263574709990270

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…We could control the robot to the goal position from the invariant space to the robot motion space by this visual servoing law. The robot visual positioning task will be accomplished once the image features in the invariant space are consistent with themselves at the desired position [16] . The main aim of the zooming control (see details in subsection 3.2) is to ensure the interest object is lying in the field of view of the camera during the visual servoing.…”

Section: Basic Principle Of the Depth Adaptive Zooming Visual Servmentioning

confidence: 99%

Depth Adaptive Zooming Visual Servoing for a Robot with a Zooming Camera

Xin

Chen

Lei

et al. 2013

International Journal of Advanced Robotic Systems

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To solve the view visibility problem and keep the observed object in the field of view (FOV) during the visual servoing, a depth adaptive zooming visual servoing strategy for a manipulator robot with a zooming camera is proposed. Firstly, a zoom control mechanism is introduced into the robot visual servoing system. It can dynamically adjust the camera's field of view to keep all the feature points on the object in the field of view of the camera and get high object local resolution at the end of visual servoing. Secondly, an invariant visual servoing method is employed to control the robot to the desired position under the changing intrinsic parameters of the camera. Finally, a nonlinear depth adaptive estimation scheme in the invariant space using Lyapunov stability theory is proposed to estimate adaptively the depth of the image features on the object. Three kinds of robot 4DOF visual positioning simulation experiments are conducted. The simulation experiment results show that the proposed approach has higher positioning precision.

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Section: Basic Principle Of the Depth Adaptive Zooming Visual Servmentioning

confidence: 99%

Depth Adaptive Zooming Visual Servoing for a Robot with a Zooming Camera

Xin

Chen

Lei

et al. 2013

International Journal of Advanced Robotic Systems

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“…Vision-based robotic technology is widely used in many fields [4]; typical examples include robotic-assisted minimally invasive surgery [5,6], robotic object catching [7], and on-orbit services [8]. Robot vision systems involve eye-in-hand, eye-to-hand, and hybrid forms [9,13], in which the robot uses image information as feedback to perform related tasks. Such a system including a robot and a sensor (one or more cameras) equipped at the end of the robot is a typical hand-eye system.…”

Section: Introductionmentioning

confidence: 99%

Optimal robot-world and hand-eye calibration with rotation and translation coupling

Wang

Song

2022

Robotica

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A classic hand-eye system involves hand-eye calibration and robot-world and hand-eye calibration. Insofar as hand-eye calibration can solve only hand-eye transformation, this study aims to determine the robot-world and hand-eye transformations simultaneously based on the robot-world and hand-eye equation. According to whether the rotation part and the translation part of the equation are decoupled, the methods can be divided into separable solutions and simultaneous solutions. The separable solutions solve the rotation part before solving the translation part, so the estimated errors of the rotation will be transferred to the translation. In this study, a method was proposed for calculation with rotation and translation coupling; a closed-form solution based on Kronecker product and an iterative solution based on the Gauss–Newton algorithm were involved. The feasibility was further tested using simulated data and real data, and the superiority was verified by comparison with the results obtained by the available method. Finally, we improved a method that can solve the singularity problem caused by the parameterization of the rotation matrix, which can be widely used in the robot-world and hand-eye calibration. The results show that the prediction errors of rotation and translation based on the proposed method be reduced to $0.26^\circ$ and $1.67$ mm, respectively.

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“…In [18], the Takagi-Sugeno fuzzy framework is used to model the IBVS for eye-in-hand camera configuration, and the model uncertainty associated with the system can be compensated. In [19,20], the eye-hand relationship via auto disturbance-rejection controller (ADRC) was investigated, where the model uncertainties and external disturbances are estimated and suppressed through the extended state observer, but there are many control parameters to be adjusted in the ADRC based approaches, which brings great challenges for the implementation of the IBVS controller. A robust IBVS control method based on disturbance observer (DOB) is proposed in [21], where the DOB is used to estimate and reject the model uncertainties and external disturbances in the IBVS system.…”

Section: Introductionmentioning

confidence: 99%

“…A robust IBVS control method based on disturbance observer (DOB) is proposed in [21], where the DOB is used to estimate and reject the model uncertainties and external disturbances in the IBVS system. However, the above-mentioned IBVS control approaches [13][14][15][16][17][18][19][20][21] are all designed at kinematic level, and they do not take the nonlinear robot dynamics into account.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network control for image‐based visual servoing of robot manipulators

Qiu¹,

Wu²

2022

IET Control Theory & Appl

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This paper presents a novel adaptive neural network control strategy for image-based visual servoing (IBVS) of robotic manipulators with both eye-in-hand and eye-to-hand camera configurations in the presence of unknown dynamics and external disturbances. The IBVS method is combined with the adaptive neural network to construct the proposed adaptive neural network controller to solve the visual servoing control problem of robots. The adaptive neural network based IBVS controller is designed based on the depth-independent interaction matrix, which can be trained on-line to identify the visual servoing robotic system modeling errors. Moreover, the proposed method can approach the unknown nonlinear dynamics for both eye-in-hand and eye-to-hand camera configurations without requiring the robot dynamics to be linearly parameterizable, and the exact knowledge of the robot structure is not needed. On the basis of the nonlinear robot dynamics, the Lyapunov stability analysis is given to prove the asymptotical convergence of the image position and velocity errors. Simulation results for both camera configurations are provided to demonstrate the performance of the proposed adaptive neural network based approach.

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Convergence analysis for the uncalibrated robotic hand–eye coordination based on the unmodeled dynamics observer

Cited by 9 publications

References 20 publications

Depth Adaptive Zooming Visual Servoing for a Robot with a Zooming Camera

Depth Adaptive Zooming Visual Servoing for a Robot with a Zooming Camera

Optimal robot-world and hand-eye calibration with rotation and translation coupling

Adaptive neural network control for image‐based visual servoing of robot manipulators

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