Image Processing for Robotic Control in Life Science Laboratory Automation

Haoying Yu; Vilhelm Krarup Moller; Rasmus John Normand Frandsen; Marjan Mansourvar

doi:10.1109/ICRCV59470.2023.10329251

Image Processing for Robotic Control in Life Science Laboratory Automation

Haoying Yu^*, Vilhelm Krarup Moller, Rasmus John Normand Frandsen, Marjan Mansourvar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

In the contemporary age of advanced technology, image processing plays a vital role as an efficient and indispensable tool across industries, enterprises, monitoring systems, and various applications. This study focuses on employing image processing techniques with an Opentrons industrial robot to ensure quality control during production. By leveraging the YOLOv5 model, the robot adeptly recognizes diverse pipette tip types, identifies liquids by their colors, and accurately measures liquid levels to estimate volume percentages within each tip. These detection results then guide a programmable robot, enabling it to perform specific actions on the tips based on predetermined parameters. This paper provides a comprehensive description of the methodology, implementation, and evaluation of our integrated control-robotic system, highlighting its potential to significantly enhance efficiency and precision in laboratory settings.

Original language	English
Title of host publication	Proceedings of 2023 5th International Conference on Robotics and Computer Vision (ICRCV)
Number of pages	5
Publisher	IEEE
Publication date	2023
Pages	305-309
ISBN (Print)	979-8-3503-2636-9
DOIs	https://doi.org/10.1109/ICRCV59470.2023.10329251
Publication status	Published - 2023
Event	2023 5th International Conference on Robotics and Computer Vision - Nanjing, China Duration: 15 Sept 2023 → 17 Sept 2023

Conference

Conference	2023 5th International Conference on Robotics and Computer Vision
Country/Territory	China
City	Nanjing
Period	15/09/2023 → 17/09/2023

Keywords

Image processing
Robotics control
Machine learning
Computer vision
Lab ammunition

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10.1109/ICRCV59470.2023.10329251

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title = "Image Processing for Robotic Control in Life Science Laboratory Automation",

abstract = "In the contemporary age of advanced technology, image processing plays a vital role as an efficient and indispensable tool across industries, enterprises, monitoring systems, and various applications. This study focuses on employing image processing techniques with an Opentrons industrial robot to ensure quality control during production. By leveraging the YOLOv5 model, the robot adeptly recognizes diverse pipette tip types, identifies liquids by their colors, and accurately measures liquid levels to estimate volume percentages within each tip. These detection results then guide a programmable robot, enabling it to perform specific actions on the tips based on predetermined parameters. This paper provides a comprehensive description of the methodology, implementation, and evaluation of our integrated control-robotic system, highlighting its potential to significantly enhance efficiency and precision in laboratory settings.",

keywords = "Image processing, Robotics control, Machine learning, Computer vision, Lab ammunition",

author = "Haoying Yu and Moller, {Vilhelm Krarup} and Frandsen, {Rasmus John Normand} and Marjan Mansourvar",

year = "2023",

doi = "10.1109/ICRCV59470.2023.10329251",

language = "English",

isbn = "979-8-3503-2636-9",

pages = "305--309",

booktitle = "Proceedings of 2023 5th International Conference on Robotics and Computer Vision (ICRCV)",

publisher = "IEEE",

address = "United States",

note = "2023 5th International Conference on Robotics and Computer Vision ; Conference date: 15-09-2023 Through 17-09-2023",

}

Yu, H, Moller, VK , Frandsen, RJN & Mansourvar, M 2023, Image Processing for Robotic Control in Life Science Laboratory Automation. in Proceedings of 2023 5th International Conference on Robotics and Computer Vision (ICRCV). IEEE, pp. 305-309, 2023 5th International Conference on Robotics and Computer Vision, Nanjing, China, 15/09/2023. https://doi.org/10.1109/ICRCV59470.2023.10329251

Image Processing for Robotic Control in Life Science Laboratory Automation. / Yu, Haoying; Moller, Vilhelm Krarup ; Frandsen, Rasmus John Normand et al.
Proceedings of 2023 5th International Conference on Robotics and Computer Vision (ICRCV). IEEE, 2023. p. 305-309.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Image Processing for Robotic Control in Life Science Laboratory Automation

AU - Yu, Haoying

AU - Moller, Vilhelm Krarup

AU - Frandsen, Rasmus John Normand

AU - Mansourvar, Marjan

PY - 2023

Y1 - 2023

N2 - In the contemporary age of advanced technology, image processing plays a vital role as an efficient and indispensable tool across industries, enterprises, monitoring systems, and various applications. This study focuses on employing image processing techniques with an Opentrons industrial robot to ensure quality control during production. By leveraging the YOLOv5 model, the robot adeptly recognizes diverse pipette tip types, identifies liquids by their colors, and accurately measures liquid levels to estimate volume percentages within each tip. These detection results then guide a programmable robot, enabling it to perform specific actions on the tips based on predetermined parameters. This paper provides a comprehensive description of the methodology, implementation, and evaluation of our integrated control-robotic system, highlighting its potential to significantly enhance efficiency and precision in laboratory settings.

AB - In the contemporary age of advanced technology, image processing plays a vital role as an efficient and indispensable tool across industries, enterprises, monitoring systems, and various applications. This study focuses on employing image processing techniques with an Opentrons industrial robot to ensure quality control during production. By leveraging the YOLOv5 model, the robot adeptly recognizes diverse pipette tip types, identifies liquids by their colors, and accurately measures liquid levels to estimate volume percentages within each tip. These detection results then guide a programmable robot, enabling it to perform specific actions on the tips based on predetermined parameters. This paper provides a comprehensive description of the methodology, implementation, and evaluation of our integrated control-robotic system, highlighting its potential to significantly enhance efficiency and precision in laboratory settings.

KW - Image processing

KW - Robotics control

KW - Machine learning

KW - Computer vision

KW - Lab ammunition

U2 - 10.1109/ICRCV59470.2023.10329251

DO - 10.1109/ICRCV59470.2023.10329251

M3 - Article in proceedings

SN - 979-8-3503-2636-9

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EP - 309

BT - Proceedings of 2023 5th International Conference on Robotics and Computer Vision (ICRCV)

PB - IEEE

T2 - 2023 5th International Conference on Robotics and Computer Vision

Y2 - 15 September 2023 through 17 September 2023

ER -

Image Processing for Robotic Control in Life Science Laboratory Automation

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Keywords

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