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Indoor positioning system for automated warehouse robots

Lai, Hong Pei (2022) Indoor positioning system for automated warehouse robots. Final Year Project, UTAR.

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    There has been a marked increase of interest in automation in many industries, ever since the COVID-19 pandemic has made it difficult for too many employees to be on-site in positions that require a labour-driven workforce. One such industry is warehouses or fulfilment centres, where processes such as sorting, packaging and shipping are considered labour-intensive. The restrictions placed on the number of employees allowed at the workplace has affected the efficiency of these operations negatively, causing many parcels to be delayed in shipping. Hence, it would be beneficial for these warehouses to implement an autonomous sorting system to speed up operations, allowing faster and more accurate sorting of parcels. An efficient warehouse localization system is the first step to achieving this. Localization plays the most important role in the efficient navigation of autonomous robots. To localize a mobile robot in a warehouse is to determine its position in the deployment area by using information gathered by external sensors. Previously deployed autonomous sorting robots in the market right now such as Amazon Robotics are costly because of the need to implement navigation sensors in each robot. Since the objective of this project is to reduce the cost of an autonomous sorting system, hence in project 1, we attempt to use a single webcam to perform detection of a moving object and display the coordinates on the screen. After that, we will use image stitching to combine multiple camera views to track the moving object across areas captured by different cameras.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: Q Science > Q Science (General)
    T Technology > T Technology (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Computer Science (Honours)
    Depositing User: ML Main Library
    Date Deposited: 15 Jan 2023 21:25
    Last Modified: 15 Jan 2023 21:25
    URI: http://eprints.utar.edu.my/id/eprint/4654

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