A Measurement Method for Robot Peg-in-Hole Prealignment Based on Combined Two-Level Visual Sensors

Jiang, Tao; Cui, Haihua; Cheng, Xiaosheng; Tian, Wei

doi:10.1109/tim.2020.3026802

Cited by 26 publications

(13 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A lot of work has been done on the research of industrial robots based on machine vision, and there have been many breakthrough results. The arc welding robot seam tracking control system studied by [ 10 ] uses vision sensors and image processing with neural networks to obtain seam shape data for effective seam tracking.…”

Section: Related Workmentioning

confidence: 99%

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Xie

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

At present, most of the handling industrial robots working on the production line are operated by teaching or preprogramming, which makes the flexibility of the production line poor and does not meet the flexible production requirements of the material handling system. This study proposes a solution based on adding computer binocular vision to a five-axis industrial robot system. A simple and high-precision binocular camera calibration method is proposed, the kinematics of the five-axis robot is analyzed, and the target positioning is realized; the communication between the upper and lower robots is realized through Ethernet. According to the specific target, the grasping scheme of the gripper was designed; the control software was developed using two schemes. Visual control is carried out by operating specific buttons on the control panel, and visual control is carried out by executing the macrovariable program, finally realizing the joint fusion of multisensor data and binocular vision.

show abstract

Section: Related Workmentioning

confidence: 99%

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Xie

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Sheet metal parts are widely used in automobile transportation, aerospace and other fields because of their excellent performance such as low density, high strength and good forming ability. Hole contour information is common and important feature in sheet metal parts that requires 3D measurement to ensure forming quality and product safety performance [1][2][3] . Existing 3D contour measurement methods for holes can be mainly divided into two categories: 1) Contour reconstruction based on measurement points 4 .…”

Section: Introductionmentioning

confidence: 99%

Fringe projection based 3D contour measurement method for holes on sheet metal parts

Zhang

Huang

et al. 2023

International Conference on Optical and Photonic Engineering (icOPEN 2022)

View full text Add to dashboard Cite

Holes are common and important feature information in sheet metal parts, and play a decisive role in product production and assembly. The 3D contour reconstruction method based on passive binocular vision has been widely used in hole measurement due to its high accuracy and robustness. It can obtain dimensional data such as diameter and center of round holes, but there are still problems in measurement accuracy and integrity, and it is difficult to accurately measure specialshaped holes. Aiming at these issues, a fringe projection based 3D contour measurement method for holes on sheet metal parts is proposed. In this method, two constant movements are performed on the extracted contour curve to the surrounding area, and the 3D contour points can be reconstructed according to the phase matching. Therefore, the real 3D contour is reconstructed inversely according to the corresponding points of the previous two reconstructed contours. With this method, contours only need to be extracted on one image, so it can reduce the effect of inconsistency in contour extraction. In addition, the accuracy and integrity of the 3D contour can be guaranteed by using high-quality phase matching of the sheet metal area. The experimental results show that this method can achieve accurate and complete 3D contour reconstruction for sheet metal holes of various shapes, and the accuracy can reach 0.08mm.

show abstract

“…These concepts of vision-based feature detection and workpiece localization are also widely adopted in other various manufacturing tasks [11,28]. For instance, [29] proposed a visual guidance system for robotic a peg-in-hole application consisting of four cameras: two in an eye-to-hand configuration for the localization of the robotic tool, while the others are in an eye-in-hand configuration and are used for alignment of the tool with reference holes. A multi-view approach was presented in [30] for the localization of target objects in a pick-and-place framework with sub-millimeter level accuracy.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems

Ayyad¹,

Halwani²,

Swart³

et al. 2022

Preprint

View full text Add to dashboard Cite

The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for faster and more reliable machining operations, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece's pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling endeffector and drills the target holes on the workpiece using a combined position-based and imagebased visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with an average positional errors of less than 0.1 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution.

show abstract

A Measurement Method for Robot Peg-in-Hole Prealignment Based on Combined Two-Level Visual Sensors

Cited by 26 publications

References 47 publications

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Fringe projection based 3D contour measurement method for holes on sheet metal parts

Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems

Contact Info

Product

Resources

About