An Uncalibrated Visual Servo Method Based on Projective Homography

Gong, Zeyu; Tao, Bo; Yang, Hua; Yin, Zhouping; Ding, Han

doi:10.1109/tase.2017.2702195

Cited by 37 publications

(18 citation statements)

References 40 publications

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“…In the original PHUVS strategy proposed by Zeyu Gong [23], the homography-based task function is defined asĥ…”

Section: Improved Homography-based Task Functionmentioning

confidence: 99%

“…To guarantee this, the constraint det(Ĥ) = 1 is defined to obtain the unique projective homography matrixĤ between the current and desired feature points in each iteration. It can be proved that e = 0 if and only if rotation matrix R = I 3×3 and position vector t = 0 [23].…”

Section: Improved Homography-based Task Functionmentioning

confidence: 99%

“…Compared with the position-based visual servo and hybrid visual servo, which both require 3-D reconstruction and are highly sensitive to calibration error, an image-based visual servo is more applicable to this uncalibrated situation [21,22]. An image-based uncalibrated visual servo (IBUVS) uses image information such as the pixel coordinates of target feature points to construct task functions directly and design a controller based on the image Jacobian, which is estimated online in each control period [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, image transmission noise and the resulting image processing errors also impact the accuracy of visual servos. To solve these problems, researchers have proposed an uncalibrated visual servo based on projective homography (PHUVS) [23]. This method solves the homography matrix by matching as many feature points as possible and uses the elements of the homography matrix to construct the task function.…”

Section: Introductionmentioning

confidence: 99%

“…The online estimation of the image Jacobian is another pivotal point of an uncalibrated visual servo. Classic image Jacobian estimation methods include the dynamic Broyden-Gauss-Newton method [37], Broyden-RLS method [29], group-based Broyden method [38], Kalman filter (KF) method [23,26,27,36], and particle filter [33,38]. Hao [22] showed that the KF method performs better than other methods when tracking targets with changing directions of motion.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improved Uncalibrated Visual Servo Strategy for Hyper-Redundant Manipulators in On-Orbit Automatic Assembly

Zhu

Cao

et al. 2020

Applied Sciences

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Aiming at on-orbit automatic assembly, an improved uncalibrated visual servo strategy for hyper-redundant manipulators based on projective homography is proposed. This strategy uses an improved homography-based task function with lower dimensions while maintaining its robustness to image defects and noise. This not only improves the real-time performance but also makes the joint space of hyper-redundant manipulator redundant to the homography-based task function. Considering that the kinematic parameters of the manipulator are easily changed in a space environment, the total Jacobian between the task function and manipulator joints is estimated online and used to construct a controller to directly control the manipulator joints without a kinematics model. When designing the controller, the above-mentioned redundancy is exploited to solve the problem of the over-limiting of the joint angles of the manipulator. The KF-SVSF method, which combines the optimality of the Kalman filter (KF) and the robustness of the smooth variable structure filter (SVSF), is introduced to the field of uncalibrated visual servos for the first time to perform the online estimation of the total Jacobian. In addition, the singular value filtering (SVF) method is adopted for the first time to avoid the interference caused by the unstable condition number of the estimated total Jacobian. Finally, simulations and experiments verify the superiority of this strategy in terms of its real-time performance and precision.

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“…In the original PHUVS strategy proposed by Zeyu Gong [23], the homography-based task function is defined asĥ…”

Section: Improved Homography-based Task Functionmentioning

confidence: 99%

Section: Improved Homography-based Task Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Uncalibrated Visual Servo Strategy for Hyper-Redundant Manipulators in On-Orbit Automatic Assembly

Zhu

Cao

et al. 2020

Applied Sciences

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Homography‐based uncalibrated visual servoing with neural‐network‐assisted robust filtering scheme and adaptive servo gain

Wang

et al. 2022

Asian Journal of Control

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In this paper, a homography‐based uncalibrated visual servo system with neural‐network‐assisted robust filtering scheme and adaptive servo gain is presented. This system employs a homography‐based task function which is robust to image defects. A neural‐network‐assisted robust filtering method which combines the new form of smooth variable structure filter (SVSF) with a radial basis function (RBF) neural network is proposed to estimate the total Jacobian between task function and robot joints. The RBF neural network in this filtering method plays the role as a corrector to further improve the accuracy and compensate the interference caused by the measurement errors of image features. The controller that directly controls the robot joints based on the estimated total Jacobian is designed for achieving the robustness to robot parameters errors. By adopting this filtering scheme, the visual servo system shows better accuracy and convincing anti‐interference ability. In addition, a novel Q‐learning strategy is introduced for this homography‐based system to make adaptive adjustment for the servo gain. This adaptive gain enables the system to achieve a faster convergence speed while ensuring the accuracy. Several simulations and experiments have been carried out to verify the performance of the proposed system.

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