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Integrating Traditional and Advanced Sensor Solutions in the Perception System of Industrial AGVs
- 1 Jinan Ansheng School, Shandong, China
* Author to whom correspondence should be addressed.
Abstract
This paper examines the perception systems used in industrial Automated Guided Vehicles (AGVs), focusing on traditional and advanced sensor solutions. Traditional perception methods, such as track-based and magnetic tape guidance, offer reliability but are limited in flexibility. In contrast, radar, vision, and LiDAR sensors provide enhanced perception capabilities, enabling AGVs to navigate safely and efficiently in complex industrial environments. The study explores various sensors, including visible light, infrared, ultrasonic, LiDAR, magnetic strip sensors, Inertial Measurement Units (IMUs), tactile sensors, Ultrawideband (UWB) sensors, thermal sensors, and millimeter-wave (mmWave) sensors, highlighting their principles, advantages, and limitations. The integration of these sensors supports robust navigation and operational efficiency in diverse settings. The methodology involves reviewing existing literature and analyzing current technologies used in industrial AGVs. Results indicate that while traditional solutions are reliable, advanced sensor technologies significantly enhance AGV performance. The paper concludes that the future of AGV perception systems lies in the integration of advanced sensors with artificial intelligence and machine learning algorithms, promoting intelligent and adaptive industrial automation. Additionally, it underscores the necessity of developing robust sensor fusion techniques to harness the full potential of these advanced sensors.
Keywords
Automated Guided Vehicles, perception systems, sensor fusion, industrial automation
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Cite this article
Shi,G. (2024).Integrating Traditional and Advanced Sensor Solutions in the Perception System of Industrial AGVs.Applied and Computational Engineering,93,15-21.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of Machine Learning assisted Automation Sensing System - CONFMLA 2024
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