Advanced, Innovative AIoT and Edge Computing for Unmanned Vehicle Systems in Factories

Yen Hui Kuo, Eric Hsiao Kuang Wu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today’s unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.

Original languageEnglish
Article number1843
JournalElectronics (Switzerland)
Volume12
Issue number8
DOIs
StatePublished - Apr 2023

Keywords

  • Internet of Things
  • artificial intelligence
  • edge computing
  • image recognition
  • smart factories
  • unmanned vehicle

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