Dynamic Visual Feedback Control For A Hand-eye Manipulator

Hashimoto, Koichi; Ebine, Takumi; Kimura, Hidenori

doi:10.1109/iros.1992.601493

Cited by 35 publications

(12 citation statements)

References 14 publications

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“…In conventional approaches, some researchers have presented methods to control the manipulator position with respect to the object or to track the feature points of an object using a hand-eye system as an application of visual servoing (Chaumette, 1991 ;Hashimoto, 1992). These methods maintain or achieve a desired relative position between the camera and the object by monitoring feature points on the object from the camera (Bernard, 1992 ;Weiss, 1987).…”

Section: Visual Feedback Controlmentioning

confidence: 99%

Image-based visual servoing control of a SCARA robot

Han

Lee

Hashimoto

2000

KSME International Journal

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In this paper, we present a new approach to visual feedback control using image-based visual servoing with stereo vision. In order to control the position and orientation of a robot with respect to an object, a new technique is proposed using binocular stereo vision. The stereo vision enables us to calculate an exact image Jacobian not only around a desired location but also at other locations. The suggested technique can guide a robot manipulator to the desired location without providing a priori knowledge such as the relative distance to the desired location or the model of an object even when the initial positioning error is large. This paper describes a model of stereo vision and how to generate feedback commands. The performance of the proposed visual servoing system is illustrated by experimental results and compared with conventional control methods for an assembly robot.

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Section: Visual Feedback Controlmentioning

confidence: 99%

Image-based visual servoing control of a SCARA robot

Han

Lee

Hashimoto

2000

KSME International Journal

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“…The nonlinear forces not only decline the control accuracy but also cause instability of the system. Dynamic visual servoing were studied by Hashimoto et al [8] and Zergeroglu et al [9], provided that the camera intrinsic and extrinsic parameters are calibrated. To cope with uncalibrated problems, Astolfi et al [10], Bishop et al [1], and Kelly [11] proposed to employ an adaptive algorithm to estimate the unknown camera parameters on-line.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Visual Tracking with Eye-in-hand Camera

Wang

Liu

2007

2007 IEEE International Conference on Integration Technology

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This paper presents a new controller for locking a moving object in 3-D space at a particular position (for example the center) on the image plane of a camera mounted on a robot by actively moving the camera. The controller is designed to cope with both the highly nonlinear robot dynamics and unknown motion of the object. Based on the fact that the unknown position of the moving object appears linearly in the closed-loop dynamics of the system if the depth-independent image Jacobian is used, we developed a nonlinear observer to estimate the 3-D motion of the object on-line. With a full consideration of dynamic responses of the robot manipulator, we employed the Lyapunov method to prove asymptotic convergence of the image errors. Both simulation and experimental results are presented to support the approach in this paper.

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“…The major problem of this approach is the choice and the extraction of the visual features to be used in the control loop. The referenced works performed ( [24], [25], [32], [27], [32]) consider shape features as measure, perimeter, etc., or a finite number of specific shape characteristics if they use models as segments, ellipsoids, etc. This emphasizes the difficulty for choosing significative shape parameters which means that no general consistent criteria is furnished to compare domains.…”

Section: Introductionmentioning

confidence: 99%

Mutational equations for shapes and vision-based control

Doyen

1995

J Math Imaging Vis

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Abstract. Basic idea of vision-based control in robotics is to include the vision system directly in the control servo loop of the robot. When images are binary, this problem corresponds to the control of the evolution of a geometric domain. The present paper proposes mathematical tools derived from shape analysis and optimization to study this problem in a quite general way, i.e., without any regularity assumptions or models a priori on the domains that we deal with. Indeed, despite the lackness of a vectorial structure, one can develop a differential calculus in the metric space of all non-empty compact subsets of a given domain of R n, and adapt ideas and results of classical differential systems to study and control the evolution of geometric domains. For instance, a shape Lyapunov characterization allows to investigate the asymptotic behavior of these geometric domains using the notion of directional shape derivative. We apply this in R 2 to the visual servoing problem using the optical flow equations and some experimental simulations illustrate this approach.

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Dynamic Visual Feedback Control For A Hand-eye Manipulator

Cited by 35 publications

References 14 publications

Image-based visual servoing control of a SCARA robot

Image-based visual servoing control of a SCARA robot

Dynamic Visual Tracking with Eye-in-hand Camera

Mutational equations for shapes and vision-based control

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