• Title/Summary/Keyword: Upper Confidence bounds applied to Trees

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Design and Implementation of Space Adaptive Autonomous Driving Air Purifying Robot for Green Smart Schools (그린 스마트 스쿨을 위한 공간 적응형 자율주행 공기청정 로봇 설계 및 구현)

  • Oh, Seokju;Lee, Jaehyeong;Lee, Chaegyu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.22 no.1
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    • pp.77-82
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    • 2022
  • The effect of indoor air pollution on the human body is greater and more dangerous than outdoor air pollution. In general, a person stays indoors for a long time, and in a closed room, pollutants are continuously accumulated and the polluted air is better delivered to the lungs. Especially in the case of young children, it is very sensitive to indoor air and it is fatal. In addition, methods to reduce indoor air pollution, which cannot be ventilated with more frequent indoor activities and continuously increasing external fine dust due to Covid 19, are becoming more important. In order to improve the problems of the existing autonomous driving air purifying robot, this paper divided the map and Upper Confidence bounds applied to Trees(UCT) based algorithm to solve the problem of the autonomous driving robot not sterilizing a specific area or staying in one space continuously, and the problem of children who are vulnerable to indoor air pollution. We propose a space-adaptive autonomous driving air purifying robot for a green smart school that can be improved.