Weighted selection of image features for resolved rate visual feedback control

Feddema, John T.; Lee, C.S.G.; Mitchell, O. Robert

doi:10.1109/70.68068

Cited by 180 publications

(91 citation statements)

References 13 publications

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“…A majority of the recently constructed visual servoing systems employ a single camera, typically mounted on the arm itself, e.g., [3], [4], [6], [8], [12]- [14]. A single camera minimizes the visual processing needed to perform visual servoing, however the loss of depth information complicates the control design as well as limiting the types of positioning operations than can be implemented.…”

Section: Related Workmentioning

confidence: 99%

“…A single camera minimizes the visual processing needed to perform visual servoing, however the loss of depth information complicates the control design as well as limiting the types of positioning operations than can be implemented. Prior depth estimates [4], adaptive estimation [6] or metric information on the target object (from which depth can be inferred) are common solutions to this problem. Two cameras in a stereo arrangement can be used to provide complete three-dimensional information about the environment [2], [7], [15]- [20].…”

Section: Related Workmentioning

confidence: 99%

“…In particular, if we restrict our attention to vision-based positioning relative to a static (unmoving) target, it can be argued that the basic principles for implementing visual feedback are by now well-understood [3], [4], [9]. Why is visual servoing not in wider use?…”

Section: Introductionmentioning

confidence: 99%

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A modular system for robust positioning using feedback from stereo vision

Hager

1997

IEEE Trans. Robot. Automat.

144

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Abstract-This paper introduces a modular framework for robot motion control using stereo vision. The approach is based on a small number of generic motion control operations referred to as primitive skills. Each primitive skill uses visual feedback to enforce a specific task-space kinematic constraint between a robot end-effector and a set of target features. By observing both the end-effector and target features, primitive skills are able to position with an accuracy that is independent of errors in handeye calibration. Furthermore, primitive skills are easily combined to form more complex kinematic constraints as required by different applications. These control laws have been integrated into a system that performs tracking and control on a single processor at real-time rates. Experiments with this system have shown that it is extremely accurate, and that it is insensitive to camera calibration error. The system has been applied to a number of example problems, showing that modular, high precision, vision-based motion control is easily achieved with off-the-shelf hardware.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A modular system for robust positioning using feedback from stereo vision

Hager

1997

IEEE Trans. Robot. Automat.

144

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“…Discussion of the issues related to feature selection for visual servo-control applications can be found in Refs. 3,25,30 . The mapping from the set of positions and orientations of the robot tool to the corresponding image features can be computed using the projective geometry of the camera.…”

Section: Perceptual Control Manifoldmentioning

confidence: 99%

Vision-Based Robot Motion Planning Using a Topology Representing Neural Network

Zeller

Schulten

Sharma

1997

World Scientific Series in Robotics and Intelligent Systems

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The goal of integrating sensors into robot motion planning has incited recent research efforts. The Perceptual Control Manifold serves this goal extending the notion of the robot configuration space to include sensor space. In this paper, we develop a framework for sensor-based motion planning of robotic manipulators using the Topology Representing Network algorithm to develop a learned representation of the Perceptual Control Manifold. The topology preserving features of the neural network lend themselves to yield, after learning, a diffusion-based path planning strategy for flexible obstacle avoidance. We demonstrate the capabilities of topology preserving maps using an industrial robot simulator and a pneumatically driven robot arm (SoftArm).

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“…The concept was introduced in the late 80's early 90's in order to control robots thanks to cameras, Weiss et al (1987);Feddema et al (1991); Hutchinson et al (1996); Chaumette and Hutchinson (2006). Regardless the robot or task used for, the goal is to control the robot thanks to visual data gathered by a camera.…”

Section: Visual Feedback Controlmentioning

confidence: 99%

Multi-Rate Visual Feedback Robot Control

Galbis¹,

Ernesto²

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Weighted selection of image features for resolved rate visual feedback control

Cited by 180 publications

References 13 publications

A modular system for robust positioning using feedback from stereo vision

A modular system for robust positioning using feedback from stereo vision

Vision-Based Robot Motion Planning Using a Topology Representing Neural Network

Multi-Rate Visual Feedback Robot Control

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