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Journal ArticleDOI

Relative end-effector control using Cartesian position based visual servoing

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TLDR
This paper presents a complete design methodology for Cartesian position based visual servo control for robots with a single camera mounted at the end-effector and the implementation using a distributed computer architecture is described.
Abstract
This paper presents a complete design methodology for Cartesian position based visual servo control for robots with a single camera mounted at the end-effector. Position based visual servo control requires the explicit calculation of the relative position and orientation (POSE) of the workpiece object with respect to the camera. This is accomplished using image plane measurements of a number of known feature points on the object, and then applying an extended Kalman filter to obtain a recursive solution of the photogrammetric equations, and to properly combine redundant measurements. The control is then designed by specifying the desired trajectories with respect to the object and forming the control error in the end-effector frame. The implementation using a distributed computer architecture is described. An experimental system has been built and used to evaluate the performance of the POSE estimation and the position based visual servo control. Several results for relative trajectory control and target tracking are presented. Results of the experiments showing the effect of loss of some of the redundant features are also presented.

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Citations
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Journal ArticleDOI

Visual servo control. I. Basic approaches

TL;DR: This paper is the first of a two-part series on the topic of visual servo control using computer vision data in the servo loop to control the motion of a robot using basic techniques that are by now well established in the field.
Journal Article

End-to-end training of deep visuomotor policies

TL;DR: In this article, a guided policy search method is used to map raw image observations directly to torques at the robot's motors, with supervision provided by a simple trajectory-centric reinforcement learning method.
Journal ArticleDOI

Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection

TL;DR: The approach achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing, and illustrates that data from different robots can be combined to learn more reliable and effective grasping.
Journal ArticleDOI

2 1/2 D visual servoing

TL;DR: Experimental results with an eye-in-hand robotic system confirm the improvement in the stability and convergence domain of the 2 1/2 D visual servoing with respect to classical position-based and image-based visual Servoing.
Journal ArticleDOI

Real-time visual tracking of complex structures

TL;DR: The paper describes how this tracking system has been extended to provide a general framework for tracking in complex configurations and a visual servoing system constructed using this framework is presented together with results showing the accuracy of the tracker.
References
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Book

Kalman Filtering with Real-time Applications

TL;DR: Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection.
Journal ArticleDOI

Dynamic sensor-based control of robots with visual feedback

TL;DR: Simulation studies of two- and three-degree-of-freedom systems show the application of an adaptive control algorithm to overcome unknown and nonlinear relations in the feature to world space mapping.
Journal ArticleDOI

Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision

TL;DR: The authors present algorithms for robotic (eye-in-hand configuration) real-time visual tracking of arbitrary 3D objects traveling at unknown velocities in a 2D space (depth is given as known).
Book ChapterDOI

Visual control of robot manipulators – a review

Peter Corke
TL;DR: This paper attempts to present a comprehensive summary of research results in the use of visual information to control robot manipulators and related mechanisms in terms of historical context, common-ality of function, algorithmic approach and method of implementation.
Proceedings ArticleDOI

Manipulator control with image-based visual servo

TL;DR: A visual feedback control scheme, called image-based visual servo, is proposed for manipulators with cameras on their hands that is stable even under noisy conditions, while the conventional position-based servo tends to be unstable.
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