Sensor-Based Target Positioning Technologies in Industrial Automation
DOI:
https://doi.org/10.54097/yct1s747Keywords:
Sensor localization; industrial automation; target positioning.Abstract
This thesis examines the critical role of sensor-based target positioning technologies in industrial automation within the context of Industry 4.0, highlighting their importance for operational efficiency and adaptability. Emphasizing the significance of sensors in automation, the study explores the principles, capabilities, and constraints of RFID, LiDAR, and WSN technologies, each pivotal for accurate target localization. It presents a comparative analysis of these technologies, focusing on their applications across various industrial scenarios, such as production lines, warehousing, and logistics, and evaluates their effectiveness through a series of experiments. The experimental analysis reveals RFID's precision improvement within 10 centimeters and LiDAR's enhanced accuracy with light information integration. It also shows that WSN's precision is contingent on noise levels, with the distance-corrected iterative localization algorithm significantly reducing localization error. The study concludes that while advancements have been made, future research should address the limitations observed in harsh industrial environments, with an emphasis on machine learning and AI integration for data processing to navigate environmental challenges.
Downloads
References
Kalsoom T, Ramzan N, Ahmed S, Ur-Rehman M. Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0. Sensors, 2020, 20(23):6783.
Landaluce H, Arjona L, Perallos A, Falcone F, Angulo I, Muralter F. A Review of IoT Sensing Applications and Challenges Using RFID and Wireless Sensor Networks. Sensors, 2020; 20(9):2495.
Kumar S. Performance Analysis of RSS-Based Localization in Wireless Sensor Networks. Wireless Personal Communications, 2019, 108(2):769-783.
K. Ma, H. Zhang, R. Wang and Z. Zhang. Target tracking system for multi-sensor data fusion.2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, 2017, pp. 1768-1772.
Mohammed Shurrab, Shakti Singh, Rabeb Mizouni, Hadi Otrok. IoT Sensor Selection for Target Localization: A Reinforcement Learning based Approach, Ad Hoc Networks, Volume 134, 2022, 102927, ISSN 1570-8705.
P. Lu and F. Dai. An Overview of Multi-sensor Information Fusion. 2021 6th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Oita, Japan, 2021, pp. 5-9.
Ali Motamedi, Mohammad Mostafa Soltani, Amin Hammad, Localization of RFID-equipped assets during the operation phase of facilities, Advanced Engineering Informatics, Volume 27, Issue 4, 2013, Pages 566-579, ISSN 1474-0346.
I.F. Akyildiz, M.C. Vuran, Wireless Sensor Networks: Principles, Design and Applications, Springer, London, 2014, ISBN 978-1-4471-5504-1, [Online].
M. H. Riaz, S. A. Bukhari, F. Mukhtar, T. Kamal, H. Sarwar and M. U. Tahir, 3d mapping using light detection and ranging, 2017 International Multi-topic Conference (INMIC), Lahore, Pakistan, 2017, pp. 1-4.
P. Yang, PRLS-INVES: A General Experimental Investigation Strategy for High Accuracy and Precision in Passive RFID Location Systems, in IEEE Internet of Things Journal, vol. 2, no. 2, 2015, pp. 159-167.
Y. Chen et al., "Knowledge-based indoor positioning based on LiDAR aided multiple sensors system for UGVs," 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014, Monterey, CA, USA, 2014, pp. 109-114.
Chen, J., Wang, S., Ouyang, M., Chen, Y., & Xuan, Y. Iterative Positioning Algorithm of the Target Node Based on Distance Correction in WSN, 2019. Preprints. https://doi.org/10.20944/preprints201905.0296.v1.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







