Novel Robotic Arm Working-Area AI Protection System

From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensur...

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Published in:Sensors (Basel, Switzerland) Vol. 23; no. 5; p. 2765
Main Authors: Lee, Jeng-Dao, Jheng, En-Shuo, Kuo, Chia-Chen, Chen, Hong-Ming, Hung, Ying-Hsiu
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.03.2023
MDPI
Subjects:
Accuracy
AI protection system
Algorithms
Artificial intelligence
Automation
Classification
Collaboration
Datasets
Design and construction
Industrial safety
Innovations
Manufacturing
Neural networks
object detection algorithms
Occupational accidents
Occupational health and safety
Robot arms
robotic arm
Robotics
Robots
Surveillance
Technology application
working safety
YOLO
Taiwan
YOLO
AI protection system
object detection algorithms
robotic arm
working safety
ISSN:1424-8220, 1424-8220
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Abstract From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.
AbstractList From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.
From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.
Audience Academic
Author Hung, Ying-Hsiu
Chen, Hong-Ming
Jheng, En-Shuo
Kuo, Chia-Chen
Lee, Jeng-Dao
AuthorAffiliation 1 Department of Automation Engineering, National Formosa University, Yunlin County 632, Taiwan
2 Doctor’s Program of Smart Industry Technology Research and Design, National Formosa University, Yunlin County 632, Taiwan
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Cites_doi 10.1109/ACCESS.2021.3077499
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2023 by the authors. 2023
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AI protection system
object detection algorithms
robotic arm
working safety
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StartPage 2765
SubjectTerms Accuracy
AI protection system
Algorithms
Artificial intelligence
Automation
Classification
Collaboration
Datasets
Design and construction
Industrial safety
Innovations
Manufacturing
Neural networks
object detection algorithms
Occupational accidents
Occupational health and safety
Robot arms
robotic arm
Robotics
Robots
Surveillance
Technology application
working safety
YOLO
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Title Novel Robotic Arm Working-Area AI Protection System
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