Evaluation of the GPS Neo Ublox M8N and Four-Sided Ultrasonic Sensor for Smart Navigation: A Case Study of a Miniature Unmanned Ground Vehicle
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The rapid advancement of autonomous systems has driven the development of intelligent navigation technologies across various fields, including transportation, robotics, and environmental monitoring. However, many autonomous ground vehicle platforms rely on high-cost sensors and complex system architectures, limiting their accessibility for research and education purposes. To address this challenge, this study proposes a cost-effective miniature Unmanned Ground Vehicle (UGV) integrating a Neo Ublox M8N GPS module with a four-sided ultrasonic sensing system to support real-time navigation and local obstacle awareness. The proposed system combines global positioning data with multi-directional short-range distance detection, processed through a Raspberry Pi and visualized via a web-based platform for real-time monitoring. Experimental testing was conducted under controlled outdoor and indoor conditions to evaluate GPS positioning accuracy, ultrasonic detection performance, and overall system responsiveness. The Neo Ublox M8N module achieved an average positional error of 4.35 m, corresponding to an accuracy of 97.4%, representing an improvement over previous studies using low-cost GPS receivers without algorithmic enhancement. Meanwhile, the ultrasonic sensors demonstrated reliable obstacle detection within a range of 5–70 cm, with an error of less than 1% and stable readings across all four sides of the UGV. The integration of these two sensing modalities demonstrated effective coordination between global and local navigation tasks, enabling real-time path visualization and obstacle awareness. Overall, the findings indicate that the proposed miniature UGV provides a scalable, low-cost platform suitable for research, prototyping, and education applications in autonomous navigation. This work also contributes practical insights for developing intelligent sensing architectures in small-scale robotic systems and highlights opportunities for further enhancements through sensor fusion and autonomous control strategies.
[1] R. M. Oosthuizen, “The Fourth Industrial Revolution – Smart Technology, Artificial Intelligence, Robotics and Algorithms: Industrial Psychologists in Future Workplaces,” Front Artif Intell, vol. 5, Jul. 2022, doi: 10.3389/frai.2022.913168.
[2] R. Sekhar, P. Shah, and I. Iswanto, “Robotics in Industry 4.0: A Bibliometric Analysis (2011-2022),” Journal of Robotics and Control (JRC), vol. 3, no. 5, pp. 583–613, Sep. 2022, doi: 10.18196/jrc.v3i5.15453.
[3] M. Soori, F. K. G. Jough, R. Dastres, and B. Arezoo, “AI-Based Decision Support Systems in Industry 4.0, A Review,” Journal of Economy and Technology, Aug. 2024, doi: 10.1016/j.ject.2024.08.005.
[4] A. Samuels, “Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: a systematic literature review,” 2024, Frontiers Media SA. doi: 10.3389/frai.2024.1477044.
[5] P. Costa, J. Ferdiansyah, and H. D. Ariessanti, “Integrating Artificial Intelligence for Autonomous Navigation in Robotics,” International Transactions on Artificial Intelligence (ITALIC), vol. 3, no. 1, pp. 64–75, Nov. 2024, doi: 10.33050/italic.v3i1.657.
[6] D. Yusuf, E. Supraptono, and A. Suryanto, “Deep Reinforcement Learning for Autonomous System Optimization in Indonesia: A Systematic Literature Review,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 3, pp. 1189–1202, Jun. 2025, doi: 10.52436/1.jutif.2025.6.3.4446.
[7] S. J. Oks et al., “Cyber-Physical Systems in the Context of Industry 4.0: A Review, Categorization and Outlook,” Information Systems Frontiers, vol. 26, no. 5, pp. 1731–1772, Oct. 2024, doi: 10.1007/s10796-022-10252-x.
[8] D. Wardhani, M. Sahat, H. Simarangkir, M. S. Safarudin, A. Y. Vandika, and F. M. Kaaffah, “The Integration of AI and IoT in Cyber-Physical Systems for Smart Manufacturing in Indonesia,” The Eastasouth Journal of Information System and Computer Science, vol. 2, no. 03, pp. 352–361, 2025, doi: 10.58812/esiscs.v2i03.
[9] J. Zhao et al., “Autonomous driving system: A comprehensive survey,” May 15, 2024, Elsevier Ltd. doi: 10.1016/j.eswa.2023.122836.
[10] A. A., S. Shirwal., Satish., Maheshwari., and N. G., “Advancements & Challenges in Unmanned Ground Vehicles,” Archives of Current Research International, vol. 25, no. 8, pp. 619–634, Aug. 2025, doi: 10.9734/acri/2025/v25i81445.
[11] L. Ode Murgazali Bakasa, S. Martha, S. Arief, and M. Ihsan, “Systematic Literature Review on the Implementation of Unmanned Ground Vehicle System (UGVS) for Defense in Indonesia,” International Journal Of Humanities Education And Social Sciences (IJHESS), vol. 3, no. 4, pp. 2061–2068, 2024, [Online]. Available: https://ijhess.com/index.php/ijhess/
[12] J. Sawal, “Unmanned Ground Vehicle Market, By Vehicle Type (Autonomous, Remotely Operated, Hybrid), By Mobility (Wheeled, Tracked, Legged, Others), By Size (Small, Medium, Large, Heavy), By Application (Military & Defense, Commercial, Homeland Security, Mining, Agriculture, Others), By Mode of Operation (Tethered, Untethered), By End-User (Defense, Commercial, Civil, Others), and By Region, Forecast to 2034,” Jul. 2025. Accessed: Aug. 26, 2025. [Online]. Available: https://www.emergenresearch.com/industry-report/unmanned-ground-vehicle-market
[13] “Unmanned Ground Vehicle (UGV) Market’s Drivers and Challenges: Strategic Overview 2025-2033,” Aug. 2025. Accessed: Aug. 26, 2025. [Online]. Available: https://www.datainsightsmarket.com/reports/unmanned-ground-vehicle-ugv-811923#
[14] “2025 Gaps and Recommendations,” Connected Automated Driving. Accessed: Aug. 26, 2025. [Online]. Available: https://www.connectedautomateddriving.eu/standards/2021-gaps-and-recommendations/
[15] I. González-Hernández, J. Flores, S. Salazar, and R. Lozano, “Robust and Precise Navigation and Obstacle Avoidance for Unmanned Ground Vehicle,” Sensors, vol. 25, no. 14, Jul. 2025, doi: 10.3390/s25144334.
[16] B. Figgis, S. I. Azid, and D. Parlevliet, “Review of unmanned ground vehicles for PV plant inspection,” May 01, 2025, Elsevier Ltd. doi: 10.1016/j.solener.2025.113404.
[17] C.-J. Lin, B.-H. Chen, and J.-Y. Jhang, “IoT-based obstacle avoidance and navigation for UGVs in wooded environments using adaptive fuzzy artificial potential field,” Internet of Things, vol. 30, p. 101524, Mar. 2025, doi: 10.1016/j.iot.2025.101524.
[18] M. H. Zafar, S. K. R. Moosavi, and F. Sanfilippo, “Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection,” Front Robot AI, vol. 11, Jun. 2024, doi: 10.3389/frobt.2024.1356345.
[19] E. Alinezhad, V. Gan, V. W.-C. Chang, and J. Zhou, “Unmanned Ground Vehicles (UGVs)-based mobile sensing for Indoor Environmental Quality (IEQ) monitoring: Current challenges and future directions,” Journal of Building Engineering, vol. 88, p. 109169, Jul. 2024, doi: 10.1016/j.jobe.2024.109169.
[20] C. Demir, M. Danışmaz, and M. Bozdemir, “Intelligent Selection of Mobility Systems For Unmanned Ground Vehicles Through Machine Learning,” Revista de Gestão Social e Ambiental, vol. 19, no. 3, p. e011590, Mar. 2025, doi: 10.24857/rgsa.v19n3-036.
[21] J. Wang, J. Wang, R. Chen, K. Yang, B. Wu, and Y. Qi, “Formation collaborative obstacle avoidance with multiple UGVs in restricted environments based on adaptive DWA,” Expert Syst Appl, vol. 265, p. 125870, Mar. 2025, doi: 10.1016/j.eswa.2024.125870.
[22] K. Sujatha, T. K. Reddy, N. P. G. Bhavani, R. S. Ponmagal, V. Srividhya, and N. Janaki, “UGVs for Agri Spray with AI assisted Paddy Crop disease Identification,” Procedia Comput Sci, vol. 230, pp. 70–81, 2023, doi: 10.1016/j.procs.2023.12.062.
[23] C.-J. Lin, C.-Y. Pan, B.-H. Chen, C.-H. Huang, and K.-H. Tang, “Obstacle Avoidance and Navigation of Unmanned Ground Vehicles Using a Type-2 Fuzzy Neural Controller Based on Improved Mantis Search Algorithm,” Sensors and Materials, vol. 37, no. 7, p. 3219, Jul. 2025, doi: 10.18494/SAM5582.
[24] Y. Wu, Y. Ding, S. Ding, Y. Savaria, and M. Li, “Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles,” Math Probl Eng, vol. 2021, pp. 1–15, Mar. 2021, doi: 10.1155/2021/5546581.
[25] C. S. D. D. Corpuz, G. A. Cuaycong, R. G. D. Diño, R. C. Estacio, G. A. C. Señar, and A. Y. Chua, “A Novel Cost-Effective Unmanned Ground Vehicle Platform for Robotics Education,” HighTech and Innovation Journal, vol. 6, no. 1, pp. 303–327, Mar. 2025, doi: 10.28991/HIJ-2025-06-01-020.
[26] S. Moss, “NEO-M8 u-blox M8 concurrent GNSS modules Data sheet.” [Online]. Available: www.u-blox.com
[27] J. Teknik Elektro, P. Negeri Padang Jurusan Teknik Elektro Politeknik Negeri Padang, J. Limau, and K. Kunci, “Komparasi Akurasi Global Posistion System (GPS) Receiver U-blox Neo-6M dan U-blox Neo-M8N pada Navigasi Quadcopter,” Elektron Jurnal Ilmiah, vol. 12, 2020.
[28] N. M. Thamrin, M. F. Misnan, M. M. D. Nizam, and M. F. Saaid, “ASSESSING NEO-M8N GPS-BASED WAYPOINT NAVIGATION PERFORMANCE FOR AN UNMANNED SURFACE VEHICLE,” 2024.
[29] N. Noviarianto, T. Turahyo, P. Kusumartono, and A. Anwar, “Implementation of Low Cost Real Time GPS Using the Haversine Method in Fishermen Electronic Navigation,” INSTICC, Dec. 2023, pp. 284–289. doi: 10.5220/0011760500003575.
[30] “Datasheet Raspberry Pi 4 Model B.” Accessed: Aug. 27, 2025. [Online]. Available: https://datasheets.raspberrypi.com/rpi4/raspberry-pi-4-datasheet.pdf
[31] “Firebase Realtime Database.” Accessed: Aug. 27, 2025. [Online]. Available: https://firebase.google.com/docs/database
[32] “Leaflet.” Accessed: Aug. 27, 2025. [Online]. Available: https://leafletjs.com/index.html
[33] E. J. Morgan, “ HCSR04 Datasheet (PDF) - List of Unclassifed Manufacturers.” Accessed: Aug. 27, 2025. [Online]. Available: https://www.alldatasheet.com/datasheet-pdf/view/1132204/ETC2/HCSR04.html
[34] A. Subekti, B. E. Cahyono, Misto, and A. T. Nugroho, “Static characteristics analysis of ultrasonic sensor HC-SR 04 and its application to water level monitoring based on Arduino Uno,” 2022, p. 060006. doi: 10.1063/5.0108043.
[35] M. K. Rihmi, G. Bintoro, M. A. Rahman, G. Puspito, and A. Muntaha, “Accuracy Analysis of Distance Measurement Using Sonar Ultrasonic Sensor HC-SR04 on Several Types of Materials,” Journal of Enviromental Engineering and Sustainable Technology, vol. 11, no. 1, pp. 10–13, Jun. 2024, doi: 10.21776/ub.jeest.2024.011.01.2.
[36] D. Daniel and D. Lasut, “Application of The Haversine Method In The Android-Based Donation Search Application,” bit-Tech, vol. 6, no. 1, pp. 1–7, Aug. 2023, doi: 10.32877/bt.v6i1.736.
[37] U. Syaripudin, N. Fauzi, W. Uriawan, W. Z, and A. Rahman, “Haversine Formula Implementation to Determine Bandung City School Zoning Using Android Based Location Based Service,” in Proceedings of the 1st International Conference on Islam, Science and Technology, ICONISTECH 2019, 11-12 July 2019, Bandung, Indonesia, EAI, 2020. doi: 10.4108/eai.11-7-2019.2303558.
[38] N. Noviarianto, T. Turahyo, P. Kusumartono, and A. Anwar, “Implementation of Low Cost Real Time GPS Using the Haversine Method in Fishermen Electronic Navigation,” in Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science, SCITEPRESS - Science and Technology Publications, 2022, pp. 284–289. doi: 10.5220/0011760500003575.
[39] C. Wang et al., “The retarding effect of glacier degradation on the Earth’s rotation,” Front Earth Sci (Lausanne), vol. 12, Sep. 2024, doi: 10.3389/feart.2024.1390303.
[40] H. Z. Khaleel et al., “Measurement Enhancement of Ultrasonic Sensor using Pelican Optimization Algorithm for Robotic Application,” Indonesian Journal of Science and Technology, vol. 9, no. 1, pp. 145–162, Nov. 2023, doi: 10.17509/ijost.v9i1.64843.
[41] K. N. Huang, C. F. Huang, Y. C. Li, and M. S. Young, “High precision, fast ultrasonic thermometer based on measurement of the speed of sound in air,” Review of Scientific Instruments, vol. 73, no. 11, pp. 4022–4027, Nov. 2002, doi: 10.1063/1.1510576.
[42] S. R. Müller et al., “Analyzing GPS Data for Psychological Research: A Tutorial,” Adv Methods Pract Psychol Sci, vol. 5, no. 2, Apr. 2022, doi: 10.1177/25152459221082680.
[43] M. D. E. K. Gunathilaka, I. Karunathilaka, and N. Perera, “Developing an Algorithm to Improve Positioning Accuracy of Low-Cost Global Navigation Satellite System Modules,” Journal of Applied Geospatial Information, vol. 7, no. 2, pp. 1050–1058, Dec. 2023, doi: 10.30871/jagi.v7i2.6790.
[44] J.-F. D. Essiben, L. E. Ihonock, J. Matanga, and Y. Suk Joe, “Experimental Evaluation Performance of Ultrasonic Multisensor Deployment Geometries for a Data Acquisition System,” Int J Distrib Sens Netw, vol. 2024, no. 1, Jan. 2024, doi: 10.1155/2024/6585503.
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