Proceedings of the 33rd Chinese Control Conference 2014
DOI: 10.1109/chicc.2014.6896439
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An eye-in-hand stereo visual servoing for tracking and catching moving objects

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Cited by 14 publications
(15 citation statements)
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“…In the case of a traditional proportional controller, the input to the robot controller u c is designed by lettinġ e = −λ e u c = −λ J e + e (25) where J e is the image interaction matrix which relates the time variation of error e and the camera velocity and J e + is the Moore-Penrose pseudo-inverse of the interaction matrix. λ is the proportional gain for the visual controller.…”
Section: Visual Servoing Controlmentioning
confidence: 99%
See 1 more Smart Citation
“…In the case of a traditional proportional controller, the input to the robot controller u c is designed by lettinġ e = −λ e u c = −λ J e + e (25) where J e is the image interaction matrix which relates the time variation of error e and the camera velocity and J e + is the Moore-Penrose pseudo-inverse of the interaction matrix. λ is the proportional gain for the visual controller.…”
Section: Visual Servoing Controlmentioning
confidence: 99%
“…where the term (∂ e/∂t) represents the time variation of e caused by the target motion which is considered to have a constant velocity. In our case we assume that the vision system is composed of a stereo vision system with two parallel cameras which are perpendicular to the baseline [26,27]. The focal points of two cameras are apart at distance b/2 with respect to origin of sensor frame C on the baseline which means the origin of the camera frame, is in the centre of these points.…”
Section: Visual Servoing Controlmentioning
confidence: 99%
“…3 shows a schematic diagram of the aforementioned robotic visual servoing system and its components. Figure 4: A robotic visual servoing system and its components [24], (a) 6-DOF robot manipulator and the moving object, (b) Visual servoing control system…”
Section: Case Study: Design Of a Robotic Visual Servoing Systemmentioning
confidence: 99%
“…Stereo visual servoing has many advantages over the classical monocular visual servoing approaches. For example, the depth information can be recovered without using any geometrical model of the observed object [12]. It should be noted that in classical image-based visual servoing, this information is needed for the computation of the image interaction matrix.…”
Section: Introductionmentioning
confidence: 99%