Cost Effective and Easily Configurable Indoor Navigation System

  • Mohammed Yaseen Taha Department of Electrical Engineering, College of Engineering, Salahaddin University-Erbil, Erbil, Iraq
  • Qahhar Muhammad Qadir Department of Electrical Engineering, College of Engineering, Salahaddin University-Erbil, Erbil, Iraq
Keywords: Industry 4.0, Global Positioning System (GPS), Indoor Positioning System (IPS), Unmanned Ground Vehicle (UGV), Artificial intelligence (AI), Computer Vision, Deep Machine Learning.


With the advent of Industry 4.0, the trend of its implementation in current factories has increased tremendously. Using autonomous mobile robots that are capable of navigating and handling material in a warehouse is one of the important pillars to convert the current warehouse inventory control to more automated and smart processes to be aligned with Industry 4.0 needs. Navigating a robot’s indoor positioning in addition to finding materials  are examples of location-based services (LBS), and are some major aspects of Industry 4.0 implementation in warehouses that should be considered. Global positioning satellites (GPS) are accurate and reliable for outdoor navigation and positioning while they are not suitable for indoor use. Indoor positioning systems (IPS) have been proposed in order to overcome this shortcoming and extend this valuable service to indoor navigation and positioning. This paper proposes a simple, cost effective and easily configurable indoor navigation system with the help of an optical path following, unmanned ground vehicle (UGV) robot augmented by image processing and computer vision deep machine learning algorithms. The proposed system prototype is capable of navigating in a warehouse as an example of an indoor area, by tracking and following a predefined traced path that covers all inventory zones in a warehouse, through the usage of infrared reflective sensors that can detect black traced path lines on bright ground. As metionded before, this general navigation mechanism is augmented and enhanced by artificial intelligence (AI) computer vision tasks to be able to select the path to the required inventory zone as its destination, and locate the requested material within this inventory zone. The adopted AI computer vision tasks that are used in the proposed prototype are deep machine learning object recognition algorithms for path selection and quick response (QR) detection.


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Author Biographies

Mohammed Yaseen Taha, Department of Electrical Engineering, College of Engineering, Salahaddin University-Erbil, Erbil, Iraq

He has recently received MSc degree from Salahaddin Unversity-Erbil. He is an employee at the same university. His current research interests include robitics, AI, deep learning, Advanced control systems and automation. 

Qahhar Muhammad Qadir, Department of Electrical Engineering, College of Engineering, Salahaddin University-Erbil, Erbil, Iraq

He has received PhD degree from the University of Southern Queensland, Toowoomba, QLD, Australia, in 2015. He is currently an employee at both the University of Kurdistan Hewlêr and Salahaddin University-Erbil. His research interests include low-power wide area networks, Internet of Things, green communication, wireless/mobile networks, quality of service/QoE enhancement, and multimedia quality assessment. He is a Local Organising Committee Member of the Annual International Telecommunication Networks and Applications Conference.


Asadi, K., Suresh, A. K., Ender, A., Gotad, S., Maniyar, S., Anand, S., Noghabaei, M., Han, K., Lobaton, E. & Wu, T. (2020). An integrated UGV-UAV system for construction site data collection. Automation in Construction, 112, 103068.
Biader Ceipidor, U., Medaglia, C. M., Serbanati, A., Azzalin, G., Barboni, M., Rizzo, F. & Sironi, M. (2009). SeSaMoNet: an RFID-based economically viable navigation system for the visually impaired. International Journal of RF Technologies, 1, 214-224.
Brena, R. F., García-Vázquez, J. P., Galván-Tejada, C. E., Muñoz-Rodriguez, D., Vargas-Rosales, C. & Fangmeyer, J. (2017). Evolution of Indoor Positioning Technologies: A Survey. Journal of Sensors, 2017, 2630413.
Cui, Y., Zhang, Y., Huang, Y., Wang, Z. & Fu, H. (2019). Novel WiFi/MEMS integrated indoor navigation system based on two-stage EKF. Micromachines, 10, 198.
Gezici, S. (2008). A survey on wireless position estimation. Wireless personal communications, 44, 263-282.
Gorostiza, E. M., Lázaro Galilea, J. L., Meca Meca, F. J., Salido Monzú, D., Espinosa Zapata, F. & Pallarés Puerto, L. (2011). Infrared sensor system for mobile-robot positioning in intelligent spaces. Sensors, 11, 5416-5438.
Kapoor, R., Ramasamy, S., Gardi, A., Bieber, C., Silverberg, L. & Sabatini, R. (2016). A novel 3D multilateration sensor using distributed ultrasonic beacons for indoor navigation. Sensors, 16, 1637.
Kim, W., Yang, S., Gerla, M. & Lee, E.-K. (2016). Crowdsource based indoor localization by uncalibrated heterogeneous Wi-Fi devices. Mobile Information Systems, 2016.
Kotanen, A., Hannikainen, M., Leppakoski, H. & Hamalainen, T. D. (2003). Experiments on local positioning with Bluetooth. Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing, 2003. IEEE, 297-303.
Kriz, P., Maly, F. & Kozel, T. (2016). Improving indoor localization using bluetooth low energy beacons. Mobile Information Systems, 2016.
Kumar, S., Qadeer, M. A. & Gupta, A. (2009). Location based services using android (LBSOID). 2009 IEEE international conference on internet multimedia services architecture and applications (IMSAA), 2009. IEEE, pp. 1-5.
Lee, C. K. M., Ip, C., Park, T. & Chung, S. (2019). A Bluetooth Location-based Indoor Positioning System for Asset Tracking in Warehouse. 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2019. IEEE, pp. 1408-1412.
Liu, S., Atia, M. M., Karamat, T. B. & Noureldin, A. (2015). A LiDAR-aided indoor navigation system for UGVs. The Journal of Navigation, 68, 253-273.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y. & Berg, A. C. (2016). SSD: Single Shot MultiBox Detector. 2016 Cham. Springer International Publishing, pp. 21-37.
Mautz, R. (2012). Indoor positioning technologies, ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Institute of Geodesy and Photogrammetry, Schweizerische Geodätische Kommission, pp. 86.
Minaeian, S., Liu, J. & Son, Y.-J. (2015). Vision-based target detection and localization via a team of cooperative UAV and UGVs. IEEE Transactions on systems, man, and cybernetics: systems, 46, 1005-1016.
Nagarajan, B., Shanmugam, V., Ananthanarayanan, V. & Sivakumar, P. B. (2020). Localization and Indoor Navigation for Visually Impaired Using Bluetooth Low Energy. Smart Systems and IoT: Innovations in Computing. Springer.
Ni, L. M., Liu, Y., Lau, Y. C. & Patil, A. P. (2003). LANDMARC: indoor location sensing using active RFID. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003.(PerCom 2003). 2003. IEEE, 407-415.
Tiemann, J. & Wietfeld, C. (2017). Scalable and precise multi-UAV indoor navigation using TDOA-based UWB localization. 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017. IEEE, 1-7.
X. Lin, T. H., C. Fang, Z. Yen, B. Yang And F. Lai, (2015). A mobile indoor positioning system based on iBeacon technology. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 4970-4973. doi: 10.1109/EMBC.2015.7319507
Xu, Y., Shmaliy, Y. S., Li, Y. & Chen, X. (2017). UWB-based indoor human localization with time-delayed data using EFIR filtering. IEEE Access, 5, 16676-16683.
Zhao, Z., Fang, J., Huang, G. Q. & Zhang, M. (2016). iBeacon enabled indoor positioning for warehouse management. 2016 4th International Symposium on Computational and Business Intelligence (ISCBI), 2016. IEEE, 21-26.
How to Cite
Taha, M., & Qadir, Q. (2021, June 30). Cost Effective and Easily Configurable Indoor Navigation System. UKH Journal of Science and Engineering, 5(1), 60-72.
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