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

Xin, Jing; Chen, Kemin; Lei, Baiying; Liu, Ding; Zhang, Jian

doi:10.5772/54566

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…The camera can change the focal length when adaptive optics are used, resulting in changes in size of the observed objects in the camera image plane. A kinematic invariant controller is proposed in [44], performing the guidance independently of the changes in the camera intrinsic parameters. However, such velocity-based controller does not take into account the system dynamics that strongly characterize the problem of the space rendezvous.…”

Section: Control In the Invariant Spacementioning

confidence: 99%

Concurrent image-based visual servoing with adaptive zooming for non-cooperative rendezvous maneuvers

Pomares

Felicetti

Pérez

et al. 2018

Advances in Space Research

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An image-based servo controller for the guidance of a spacecraft during non-cooperative rendezvous is presented in this paper. The controller directly utilizes the visual features from image frames of a target spacecraft for computing both attitude and orbital maneuvers concurrently. The utilization of adaptive optics, such as zooming cameras, is also addressed through developing an invariant-image servo controller. The controller allows for performing rendezvous maneuvers independently from the adjustments of the camera focal length, improving the performance and versatility of maneuvers. The stability of the proposed control scheme is proven analytically in the invariant space, and its viability is explored through numerical simulations.

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Section: Control In the Invariant Spacementioning

confidence: 99%

Concurrent image-based visual servoing with adaptive zooming for non-cooperative rendezvous maneuvers

Pomares

Felicetti

Pérez

et al. 2018

Advances in Space Research

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show abstract

“…The former employs the observed parameters of the visual feature in the image of geometric primitives (points, straight lines, ellipses, and cylinders) in image-based visual servoing (IBVS). [3][4][5] In position-based visual servoing (PBVS), [6][7][8] the geometric primitives reconstruct the camera pose, which then serves as input for visual servoing. The above approaches subject the image stream to an ensemble of measurement processes, including image processing, image matching, and visual tracking steps, from which the visual features are determined.…”

Section: Introductionmentioning

confidence: 99%

Closed-form camera pose and plane parameters estimation for moments-based visual servoing of planar objects

Chen

Luo

Han

et al. 2022

International Journal of Advanced Robotic Systems

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Image moments are global descriptors of an image and can be used to achieve control-decoupling properties in visual servoing. However, only a few methods completely decouple the control. This study introduces a novel camera pose estimation method, which is a closed-form solution, based on the image moments of planar objects. Traditional position-based visual servoing estimates the pose of a camera relative to an object, but the pose estimation method directly estimates the pose of an initial camera relative to a desired camera. Because the estimation method is based on plane parameters, a plane parameters estimation method based on the 2D rotation, 2D translation, and scale invariant moments is also proposed. A completely decoupled position-based visual servoing control scheme from the two estimation methods above was adopted. The new scheme exhibited asymptotic stability when the object plane was in the camera field of view. Simulation results demonstrated the effectiveness of the two estimation methods and the advantages of the visual servo control scheme compared with the classical method.

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“…To overcome the shortcomings of the trajectory planning method to solve the problem of "FOV constraints," researchers at home and abroad put forward a series of nontrajectory planning methods. By combining different visual servo structures or designing a new VS structure, the object is always in the camera FOV, such as combining IBVS and PBVS approach, 21 switching approach, 22 zooming VS approach, 23 direct VS approach, 24 and IBVS with a Kalman-neural-network filtering approach. 25 Besides, some advanced control laws are designed to deal with the FOV constraint problem, such as sliding mode control, 26,27 model predictive control, [28][29][30] prescribed performance control, 31,32 switched controller, 6,33 and so on.…”

Section: Introductionmentioning

confidence: 99%

Visual servoing of robot manipulator with weak field-of-view constraints

Xin¹,

Han²,

Bao-jing³

2021

International Journal of Advanced Robotic Systems

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Aiming at the problem of servoing task failure caused by the manipulated object deviating from the camera field-of-view (FOV) during the robot manipulator visual servoing (VS) process, a new VS method based on an improved tracking learning detection (TLD) algorithm is proposed in this article, which allows the manipulated object to deviate from the camera FOV in several continuous frames and maintains the smoothness of the robot manipulator motion during VS. Firstly, to implement the robot manipulator visual object tracking task with strong robustness under the weak FOV constraints, an improved TLD algorithm is proposed. Then, the algorithm is used to extract the image features (object in the camera FOV) or predict image features (object out of the camera FOV) of the manipulated object in the current frame. And then, the position of the manipulated object in the current image is further estimated. Finally, the visual sliding mode control law is designed according to the image feature errors to control the motion of the robot manipulator so as to complete the visual tracking task of the robot manipulator to the manipulated object in complex natural scenes with high robustness. Several robot manipulator VS experiments were conducted on a six-degrees-of-freedom MOTOMANSV3 industrial manipulator under different natural scenes. The experimental results show that the proposed robot manipulator VS method can relax the FOV constraint requirements on real-time visibility of manipulated object and effectively solve the problem of servoing task failure caused by the object deviating from the camera FOV during the VS.

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

Cited by 6 publications

References 18 publications

Concurrent image-based visual servoing with adaptive zooming for non-cooperative rendezvous maneuvers

Concurrent image-based visual servoing with adaptive zooming for non-cooperative rendezvous maneuvers

Closed-form camera pose and plane parameters estimation for moments-based visual servoing of planar objects

Visual servoing of robot manipulator with weak field-of-view constraints

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