This paper presents a new adaptive controller for dynamic tracking of a robot manipulator without visual velocity when the intrinsic and extrinsic parameters of the camera are not calibrated. Most controllers in the past require the measurement of the visual velocity or differentiation of the visual position. The measurement of the visual velocity is subject to big noises in general due to low sampling rates of the vision loop. To avoid performance decaying caused by measurement errors of the visual velocity, the controller we developed requires estimated visual velocity only. With a full consideration of dynamic responses of the robot manipulator, we employed the Lyapunov method to prove the convergence of the image errors of the trajectory to zero and the convergence of the estimated parameters to the real values up to a scale. Experiments have been conducted to demonstrate good convergence of the trajectory errors of the robot under the control of the proposed method

Cited by 16

Uncalibrated Visual Tracking Control Without Visual Velocity

Hesheng Wang, Yunhui Liu, Weidong Chen · IEEE Transactions on Control Systems Technology · 2010

This paper presents a new adaptive controller for image-based tracking of a robot manipulator without using visual velocity when the intrinsic and extrinsic parameters of the camera are not calibrated. Most of existing controllers require the measurement of the visual velocity, which is subject to big noises in general due to low sampling rates of the vision loop. To avoid performance decaying caused by measurement errors of the visual velocity we propose a new image-based tracking controller based on the proposal of an estimator of visual velocity. With a full consideration of dynamic responses of the robot manipulator, we proved the convergence of the image errors of the trajectory to zero and the convergence of the estimated parameters to the real values up to a scale by the Lyapunov method. Experiments have been conducted to demonstrate good convergence of the trajectory errors under the control of the proposed method.

Cited by 81

Delayed visual feedback and visual-motor control.

Kevin Francis. Bowen · 1976

Cited by 0

Visual Culture of Control

Robert W. Sweeny · Studies in Art Education · 2006

Current discussions regarding the notion of visual culture in art educational practice center the actions of the viewer as participant within the networks of visuality common in many contemporary societies. Surveillance technologies and techniques shift this notion of participation from active to passive, from seeing to being seen. This article explores the relationship between the gaze as structured by art educational practice and contemporary forms of surveillance, looking to theoreticians who analyze power as it is formed within complex visual networks.

Cited by 11

Task based visual control

Sage, Kingsley · OpenGrey (Institut de l'Information Scientifique et Technique) · 2006

Cited by 0Open access

Visual Control of Locomotion

Brett R. Fajen · Cambridge University Press eBooks · 2021

This Element examines visual perception in the context of activities that involve moving about in complex, dynamic environments. A central theme is that the ability of humans and other animals to perceive their surroundings based on vision is profoundly shaped by the need to adaptively regulate locomotion to variations in the environment. As such, important new insights into what and how we perceive can be gleaned by investigating the connection between vision and the control of locomotion. I present an integrated summary of decades of research on the perception of self-motion and object motion based on optic flow, the perception of spatial layout and affordances, and the control strategies for guiding locomotion based on visual information. I also explore important theoretical issues and debates, including the question of whether visual control relies on internal models.

Cited by 45

Visual control of grasping

Billibon H. Yoshimi, Peter K. Allen · Lecture notes in control and information sciences · 2007

Cited by 4

Real-Time Visual Control

Benedetto Allotta, Felice Fanizza, BUTTAZZO, Giorgio Carlo · CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) · 1993

Cited by 0

Visual Model Predictive Control

Tolani, Varun, Author · UC Berkeley

We introduce an autonomous navigation framework for ground-based, mobile robots that incorporates a known dynamics model into training, allows for planning in unknown, partially observable environments, and solves the full navigation problem of goal-directed, collision avoidant movement on a robot with complex, non-linear dynamics. We leverage visual semantics through a trained policy that, given a desired goal location and first person image of the environment, predicts a low frequency guiding control, or waypoint. We use the waypoint produced by our policy along with robust feedback controllers and known dynamics models to generate high frequency control outputs. Our approach allows for visual semantics to be learned during training while providing a simple methodology for incorporating robust dynamics models into training. Our experiments demonstrate that our method is able to reason through statistics of the visual world allowing for effective planning in unknown spaces. Additionally, we demonstrate that our formulation is robust to the particulars of low-level control, achieving performance over twice that of a comparable end-to-end learning method.

Cited by 1

VISUAL MEASURING AUTOMATION CONTROL

A.D. Tulegulov, D.S. Yergaliyev, Dmitriy Zuev et al. · Reliability and Quality of Complex Systems · 2021

Background. Practice shows that accidents and disasters occurring during the destruction of structural elements of objects, machines and products are almost always associated with the presence of defects in them. This article proposes the automation of the process of the visual measuring method (VIM) of non-destructive testing. The purpose of this work is to develop a software package that allows you to automate the work of a specialist conducting visual measurement control (VIC). Materials and methods. The option of creating a database of 3D models of the tested samples is being considered for further obtaining the trajectory of the electron microscope for creating images. Results. A database of reference images of material surfaces without defects, images of material surfaces with examples of various types of defects has been created. Conclusions. The formed base will make it possible to analyze by comparing the results obtained by artificial intelligence methods with images obtained during flaw detection with an electron microscope. This, in turn, will make it possible to predict possible equipment failures, as well as to work out the process of forming the final image, indicating the locations of possible defects.

Cited by 0Open access

Positional Visual Feedback Control

2007

Cited by 0

Introduction to Visual Control

Jian Chen · 2018

Cited by 0

Visual control of grasping.

Billibon H. Yoshimi, Peter K. Allen · 1997

Most robotic hands are either sensorless or lack the ability to accurately and robustly report position and force information relating to contact. This paper describes a system that integrates real-time computer vision with a sensorless gripper to provide closed loop feedback control for grasping and manipulation tasks. Many hand-eye coordination skills can be thought of as sensory-control loops, where specialized reasoning has been embodied as a feedback or control path in the loop’s construction. This system captures the essence of these hand-eye coordination skills in simple visual control primitives, which can be used to perform higher-level grasping and manipulation tasks. Experimental results are shown for two typical robotics tasks: the positioning task of locating, picking up, and inserting a bolt into a nut under visual control and the visual control of a bolt tightening task.

Cited by 0

2 Visual environment control

2009

Cited by 0

Control of visual attention

Harold Pashler · 2016

Cited by 36