UKH Journal of Science and Engineering (Jun 2021)

Cost Effective and Easily Configurable Indoor Navigation System

  • Mohammed Yaseen Taha,
  • Qahhar Muhammad Qadir

DOI
https://doi.org/10.25079/ukhjse.v5n1y2021.pp60-72
Journal volume & issue
Vol. 5, no. 8
pp. 60 – 72

Abstract

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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 processesto be aligned with Industry 4.0 needs. Navigating a robot’s indoor positioning in addition to finding materials areexamples of location-based services (LBS), and are some major aspects of Industry4.0 implementation in warehouses that should be considered. Global positioning satellites (GPS) areaccurate and reliable for outdoor navigation and positioning while they are not suitable forindooruse. Indoor positioning systems(IPS) havebeen 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 locatethe 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 algorithmsfor path selection and quick response (QR) detection.

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