VISION-BASED SAFETY SYSTEM FOR INDUSTRIAL COLLABORATIVE ROBOT

This paper proposes a new method to improve the safety operation of collaborative robots (cobots) by combining two existing safety features: distance-based speed control and obstacle avoidance. The system utilizes a Kinect V2 camera to capture depth data of the robot's workspace. This data is used to determine the distance between humans and the robot, allowing for speed regulation to ensure safety. Additionally, the depth data is converted into a map to enable obstacle avoidance by the robot. The proposed method was evaluated in a simulated environment using various pick-and-place scenarios. The results demonstrate the effectiveness of the system in ensuring safe human-robot collaboration. The paper also identifies limitations, such as the limited depth perception due to a single camera. Future work will explore using multiple cameras and implementing the system on a physical cobot.

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Publication Details

Journal
Journal of Engineering and Technology (JET)
Published
2024-12-31
DOI
https://doi.org/10.54554/jet.2024.15.02.016
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
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article

VISION-BASED SAFETY SYSTEM FOR INDUSTRIAL COLLABORATIVE ROBOT

Jun Hao Ho, Tick Fei Chay
Journal of Engineering and Technology (JET)
Industrial Vision Systems and Defect Detection
article

VISION-BASED SAFETY SYSTEM FOR INDUSTRIAL COLLABORATIVE ROBOT

Jun Hao Ho, Tick Fei Chay
article en

Abstract

This paper proposes a new method to improve the safety operation of collaborative robots (cobots) by combining two existing safety features: distance-based speed control and obstacle avoidance. The system utilizes a Kinect V2 camera to capture depth data of the robot's workspace. This data is used to determine the distance between humans and the robot, allowing for speed regulation to ensure safety. Additionally, the depth data is converted into a map to enable obstacle avoidance by the robot. The proposed method was evaluated in a simulated environment using various pick-and-place scenarios. The results demonstrate the effectiveness of the system in ensuring safe human-robot collaboration. The paper also identifies limitations, such as the limited depth perception due to a single camera. Future work will explore using multiple cameras and implementing the system on a physical cobot.

Journal of Engineering and Technology (JET)Vol. 15(2)
Tunku Abdul Rahman University of Management and Technology (MY)
Openalex Percentile: Top 61%
Industrial Vision Systems and Defect Detection
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