ASPG Menu
search

American Scientific Publishing Group

verified Journal

Journal of Intelligent Systems and Internet of Things

ISSN
Online: 2690-6791 Print: 2769-786X
Frequency

Continuous publication

Publication Model

Open access journal. All articles are freely available online with no APC.

Journal of Intelligent Systems and Internet of Things
Full Length Article

Volume 17Issue 2PP: 295-310 • 2025

Low-Cost Multi-Sensor Localization for Indoor AGVs

Nopparut Khaewnak 1* ,
Siripong Pawako 1 ,
Akkharachai Kosiyanurak 1 ,
Suradet Tantrairatn 1 ,
Jiraphon Srisertpol 1
1School of Mechanical Engineering, Institute of Engineering, Suranaree University of Technology), Nakhon Ratchasima, Thailand
* Corresponding Author.
verified

Open Access & Copyright

© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Received: January 01, 2025 Revised: March 01, 2025 Accepted: June 05, 2025

Abstract

Indoor transportation systems are a key area of development, where Automated Guided Vehicles (AGVs) help to increase efficiency and reduce labor costs. However, high-precision positioning technologies such as LiDAR and GNSS are expensive, making them unsuitable for widespread use. This research has developed a low-cost positioning system for indoor AGVs using multiple sensors, including CCTV, UWB, inertial measurement units (IMUs), and encoders. The experiment was carried out under both static and dynamic conditions. In static tests, Trilateration distance measurements show a lower positioning error than the triangular method, with a maximum error of 1.4464 m (x-axis) and 1.0464 m (y-axis) in dynamic tests. The integrated Encoder and IMU sensor data yielded the lowest error (RMSE = 0.0732 m at 0.4 m/s, 0.0678 at 0.27 m/s), Next is CCTV, while UWB has the highest error rate. The application of a Parallel Sensor Fusion architecture optimized using a Generalized Reduced Gradient (GRG) nonlinear algorithm, significantly reduced localization errors. The RMSE values decreased to 0.0623 m (0.4 m/s) and 0.0411 m (0.27 m/s). The results, in a controlled environment laboratory, indicate that combining multiple sensors will improve the positioning accuracy. Combining the encoder and IMU effectively reduces accumulated errors and increases system stability. While Adjust the weight of the sensor offline, this proposed system offers a cost-effective positioning solution for indoor AGVs, which contributes to the development of affordable and accurate AGV navigation systems.

Keywords

Automatic Guided Vehicles (AGVs) Positioning Technology Low-cost Sensors Multi-Sensor Fusion Localization

References

[1]       I. Kubasakova, J. Kubanova, D. Benco, and D. Kadlecová, "Implementation of Automated Guided Vehicles for the Automation of Selected Processes and Elimination of Collisions between Handling Equipment and Humans in the Warehouse," Sensors, vol. 24, no. 3, 2024.

 

[2]       A. Thakur and P. Rajalakshmi, "LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation," IEEE Open Journal of Instrumentation and Measurement, vol. 3, pp. 1-11, 2024.

 

[3]       K. W. Park and S. Y. Park, "Visual LIDAR Odometry Using Tree Trunk Detection and LIDAR Localization," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVIII-1/W2-2023, pp. 627-632, 2023.

 

[4]       N. Maitlo, N. Noonari, K. Arshid, N. Ahmed, and S. Duraisamy, "AINS: Affordable Indoor Navigation Solution via Line Color Identification Using Mono-Camera for Autonomous Vehicles," in 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 2024, pp. 1-7.

 

[5]       S. Jeong, M. Ko, and J. Kim, "LiDAR Localization by Removing Moveable Objects," Electronics, vol. 12, no. 22, 2023.

 

[6]       S. Odngam, P. Intacharoen, N. Tanman, and C. Sumpavakup, "The Design of Distance-Warning and Brake Pressure Control Systems Incorporating LiDAR Technology for Use in Autonomous Vehicles," World Electric Vehicle Journal, vol. 15, no. 12, 2024.

 

[7]       F. Sauerbeck, D. Kulmer, M. Pielmeier, M. Leitenstern, C. Weiß, and J. Betz, "Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving," in 2023 IEEE SENSORS, 2023, pp. 1-4.

 

[8]       M. Kascha, K. Xin, X. Zou, A. Sturm, R. Henze, L. Heister, and S. Oezberk, "Monocular Camera Localization for Automated Driving," in 2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 2023, pp. 1-6.

 

[9]       T. Kim, S. Lim, G. Shin, G. Sim, and D. Yun, "An Open-Source Low-Cost Mobile Robot System With an RGB-D Camera and Efficient Real-Time Navigation Algorithm," IEEE Access, vol. 10, pp. 127871-127881, 2022.

 

[10]    L. Triyono, R. Gernowo, and P. Prayitno, "Optimizing indoor navigation systems through ensemble deep learning techniques for ArUco marker detection," International Journal of Intelligent Engineering & Systems, vol. 17, no. 6, 2024.

 

[11]    X. Liu, G. Wang, and K. Chen, "High-precision vision localization system for autonomous guided vehicles in dusty industrial environments," NAVIGATION: Journal of the Institute of Navigation, vol. 69, no. 1, 2022.

 

[12]    S. Y. Alaba, "GPS-IMU Sensor Fusion for Reliable Autonomous Vehicle Position Estimation," ArXiv, vol. abs/2405.08119, 2024.

 

[13]    A. Charroud, K. El Moutaouakil, V. Palade, and A. Yahyaouy, "Enhanced autoencoder-based LiDAR localization in self-driving vehicles," Applied Soft Computing, vol. 152, p. 111225, 2024.

 

[14]    V. Brunacci, A. Dionigi, A. De Angelis, and G. Costante, "Infrastructure-less UWB-based Active Relative Localization," in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024, pp. 14079-14086.

 

[15]    K. Wongvichayakul, C. Mitsantisuk, and K. Prompol, "Development of high-precision ultra-wideband (UWB) path following using Kalman filter for automatic guide vehicles," in *Proceedings of IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society*, 2023, pp. 1-6.

 

[16]    M. Kim, Y. Kwon, S. Lee, and S.-e. Yoon, "CCTV-informed human-aware robot navigation in crowded indoor environments," IEEE Robotics and Automation Letters, vol. 9, no. 6, pp. 5767-5774, 2024.

 

[17]    C. Lundquist, Sensor Fusion for Automotive Applications, Ph.D. dissertation, Linköping University, Linköping, Sweden, 2011.

 

[18]    Y. Chandola, J. Virmani, H. S. Bhadauria, and P. Kumar, "Chapter 4 - End-to-end pre-trained CNN- based computer-aided classification system design for chest radiographs," in Deep Learning for Chest Radiographs, Primers in Biomedical Imaging Devices and Systems, Academic Press, 2021, pp. 117-140.

 

[19]    R. A. Pashchapur, Y. Chen, and D. Ignatyev, "Low–cost multi-object positioning system with optical sensor fusion," in AIAA SCITECH 2023 Forum, AIAA SciTech Forum, American Institute of Aeronautics and Astronautics, Jan. 2023.

 

[20]    W. Zhu and S. Guo, "Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR," International Journal of Sensors and Sensor Networks, vol. 12, no. 1, pp. 13-22, 2024.

 

[21]    S. Bouzoualegh, E.-H. Guechi, and R. Kelaiaia, "Model Predictive Control of a Differential-Drive Mobile Robot," Acta Universitatis Sapientiae Electrical and Mechanical Engineering, vol. 10, pp. 20-41, Dec. 2018.

 

[22]    R. Dhaouadi and A. A. Hatab, "Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework," in Proc. IEEE Int. Conf. on Robotics and Automation, vol. 02, 2013.

 

[23]    E. Alcalá, L. Sellart, V. Puig, J. Quevedo, J. Saludes, D. Vázquez, and A. López, "Comparison of two non-linear model-based control strategies for autonomous vehicles," in Proceedings of the 24th Mediterranean Conference on Control and Automation (MED), 2016, pp. 846-851.

 

[24]    F. Che, Q. Z. Ahmed, P. I. Lazaridis, P. Sureephong, and T. Alade, "Indoor Positioning System (IPS) using Ultra-Wide Bandwidth (UWB)-for Industrial Internet of Things (IIoT)," Sensors, vol. 23, no. 12, article no. 5710, 2023.

 

[25]    Z. Chen, X. Li, L. Wang, Y. Shi, Z. Sun, and W. Sun, "An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot," Applied Sciences, vol. 12, no. 22, pp. 11441, 2022.

 

[26]    L. S. Lasdon, R. L. Fox, and M. W. Ratner, "Nonlinear optimization using the generalized reduced gradient method," Recherche Opérationnelle, vol. 8, no. 3, pp. 73-103, 1974.

 

[27]    L. S. Lasdon, A. D. Waren, A. Jain, and M. Ratner, "Design and testing of a generalized reduced gradient code for nonlinear programming," ACM Transactions on Mathematical Software, vol. 4, no. 1, pp. 34-50, Mar. 1978.

 

[28]    J. S. Arora, "Chapter 13 - More on Numerical Methods for Constrained Optimum Design," in Introduction to Optimum Design (Third Edition), Academic Press, 2012, pp. 533-573.

 

[29]    D. B. Nugroho, L. P. Panjaitan, D. Kurniawati, Z. Kholil, B. Susanto, and L. R. Sasongko, "GRG non-linear and ARWM method for estimating the GARCH-M, GJR, and log-GARCH models," Jurnal Teoridan Aplikasi Matematika, vol. 6, no. 2, pp. 448-460, 2022.

 

[30]    S. J. Terregrossa and U. S¸ ener, "Employing a generalized reduced gradient algorithm method to form combinations of steel price forecasts generated separately by ARIMA-TF and ANN models," Cogent Economics & Finance, vol. 11, no. 1, 2023.

Cite This Article

Choose your preferred format

format_quote
Khaewnak, Nopparut, Pawako, Siripong, Kosiyanurak, Akkharachai, Tantrairatn, Suradet, Srisertpol, Jiraphon. "Low-Cost Multi-Sensor Localization for Indoor AGVs." Journal of Intelligent Systems and Internet of Things, vol. Volume 17, no. Issue 2, 2025, pp. 295-310. DOI: https://doi.org/10.54216/JISIoT.170219
Khaewnak, N., Pawako, S., Kosiyanurak, A., Tantrairatn, S., Srisertpol, J. (2025). Low-Cost Multi-Sensor Localization for Indoor AGVs. Journal of Intelligent Systems and Internet of Things, Volume 17(Issue 2), 295-310. DOI: https://doi.org/10.54216/JISIoT.170219
Khaewnak, Nopparut, Pawako, Siripong, Kosiyanurak, Akkharachai, Tantrairatn, Suradet, Srisertpol, Jiraphon. "Low-Cost Multi-Sensor Localization for Indoor AGVs." Journal of Intelligent Systems and Internet of Things Volume 17, no. Issue 2 (2025): 295-310. DOI: https://doi.org/10.54216/JISIoT.170219
Khaewnak, N., Pawako, S., Kosiyanurak, A., Tantrairatn, S., Srisertpol, J. (2025) 'Low-Cost Multi-Sensor Localization for Indoor AGVs', Journal of Intelligent Systems and Internet of Things, Volume 17(Issue 2), pp. 295-310. DOI: https://doi.org/10.54216/JISIoT.170219
Khaewnak N, Pawako S, Kosiyanurak A, Tantrairatn S, Srisertpol J. Low-Cost Multi-Sensor Localization for Indoor AGVs. Journal of Intelligent Systems and Internet of Things. 2025;Volume 17(Issue 2):295-310. DOI: https://doi.org/10.54216/JISIoT.170219
N. Khaewnak, S. Pawako, A. Kosiyanurak, S. Tantrairatn, J. Srisertpol, "Low-Cost Multi-Sensor Localization for Indoor AGVs," Journal of Intelligent Systems and Internet of Things, vol. Volume 17, no. Issue 2, pp. 295-310, 2025. DOI: https://doi.org/10.54216/JISIoT.170219
policy

Publisher's Note

The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.

Digital Archive Ready