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



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.


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Research Articles

How to Cite

Cost Effective and Easily Configurable Indoor Navigation System. (2021). UKH Journal of Science and Engineering, 5(1), 60-72.