International Journal of Control, Automation, and Systems 2025; 23(2): 489-497
https://doi.org/10.1007/s12555-024-0549-1
© The International Journal of Control, Automation, and Systems
This paper proposes a safety system for speed control of collaborative robots using multiple cameras and edge AI. Collaborative robots share the workspace with human workers, which requires a high level of safety for the workers. ISO/TS 15066 is a safety regulation for collaborative robots, and the proposed system establishes an additional safety system beyond the built-in safety regulations of collaborative robots. Using the edge AI-based YOLOv5n-seg, the system detects workers and estimates the distance through the instance segmentation coordinates used in object detection. The distance accuracy through the RGB-D camera shows an error rate of 3.55% at 3 meters. The adjusted base joint’s speed is 0.09 rad/s in normal mode. This is about 1/6 of the original maximum speed of 0.6 rad/s. When applying the speed reduction rate of the safety system, a robot operating at maximum speed in normal mode complies with the speed and distance regulations of ISO/TS 15066. Additionally, the robot’s response time from object detection is 0.806 seconds, confirming that worker safety can be effectively ensured.
Keywords Collaborative robot, edge AI, multiple camera, safety system.
International Journal of Control, Automation, and Systems 2025; 23(2): 489-497
Published online February 1, 2025 https://doi.org/10.1007/s12555-024-0549-1
Copyright © The International Journal of Control, Automation, and Systems.
Nak-Won Choi, Yeong-Bin Kim, and Bum Yong Park*
Kumoh National Institute of Technology
This paper proposes a safety system for speed control of collaborative robots using multiple cameras and edge AI. Collaborative robots share the workspace with human workers, which requires a high level of safety for the workers. ISO/TS 15066 is a safety regulation for collaborative robots, and the proposed system establishes an additional safety system beyond the built-in safety regulations of collaborative robots. Using the edge AI-based YOLOv5n-seg, the system detects workers and estimates the distance through the instance segmentation coordinates used in object detection. The distance accuracy through the RGB-D camera shows an error rate of 3.55% at 3 meters. The adjusted base joint’s speed is 0.09 rad/s in normal mode. This is about 1/6 of the original maximum speed of 0.6 rad/s. When applying the speed reduction rate of the safety system, a robot operating at maximum speed in normal mode complies with the speed and distance regulations of ISO/TS 15066. Additionally, the robot’s response time from object detection is 0.806 seconds, confirming that worker safety can be effectively ensured.
Keywords: Collaborative robot, edge AI, multiple camera, safety system.
Vol. 23, No. 2, pp. 359~682
Shuangning Lu, Zhouda Xu, and Binrui Wang*
International Journal of Control, Automation and Systems 2022; 20(10): 3347-3360