Robot Vision for Manipulation: A Trip to Real-World Applications

Martínez-Martín, Ester; Pobil, Ángel P. del

doi:10.1109/access.2020.3048053

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2024

Publication Types

Select...

Article4

Other1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Emerging applications demand that industrial robots not only be faster, but also be able to accurately identify and find parts that are randomly placed on moving conveyors, stacked in containers, or on pallets [14]- [16]. Machine vision systems, which have been around for decades, are now being used in conjunction with robotics to aid automation systems in the processing of such components [17], [18].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

et al. 2021

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

et al. 2021

View full text Add to dashboard Cite

show abstract

A collaborative SLAM method for dual payload-carrying UAVs in denied environments

Rao,

Liu,

Chen

et al. 2024

Machine Vision and Applications

View full text Add to dashboard Cite

Convolutional neural network-based pose mapping estimation as an alternative to traditional hand–eye calibration

Zhou

Xi-yue

et al. 2023

Review of Scientific Instruments

View full text Add to dashboard Cite

The vision system is a crucial technology for realizing the automation and intelligence of industrial robots, and the accuracy of hand–eye calibration is crucial in determining the relationship between the camera and robot end. Parallel robots are widely used in automated assembly due to their high positioning accuracy and large carrying capacity, but traditional hand–eye calibration methods may not be applicable due to their limited motion range and resulting accuracy problems. To address this issue, we propose using a pose, nonlinear mapping estimation method to solve the hand–eye calibration problem and have constructed a 1-D pose estimation convolutional neural network (PECNN) with excellent performance, through experiments and discussions. The PECNN achieves an end-to-end mapping of the variation of the target object pose to the variation of the robot end pose. Our experiments have shown that the proposed hand–eye calibration method has high accuracy and can be applied to the automated assembly tasks of vision-guided parallel robots. Moreover, the method is also applicable to most parallel robots and tandem robots.

show abstract

Robot Vision for Manipulation: A Trip to Real-World Applications

Cited by 6 publications

References 29 publications

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

A collaborative SLAM method for dual payload-carrying UAVs in denied environments

Convolutional neural network-based pose mapping estimation as an alternative to traditional hand–eye calibration

Contact Info

Product

Resources

About