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This study introduces an autonomous UV-C disinfection strategy using a dynamic Irradiation Map for real-time navigation in unknown indoor environments, improving disinfection efficacy.

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Area of Science:

  • Robotics and Automation
  • Environmental Science
  • Infectious Disease Control

Background:

  • The COVID-19 pandemic highlights the need for effective environmental disinfection against SARS-CoV-2.
  • Surface contamination by SARS-CoV-2 poses a significant risk of human infection.
  • Existing UV-C disinfection robots lack full autonomy and real-time trajectory planning in unknown spaces.

Purpose of the Study:

  • To develop a fully autonomous UV-C disinfection system for indoor environments.
  • To enable real-time trajectory computation for robots in unknown settings.
  • To enhance disinfection efficiency by targeting optimal UV-C doses.

Main Methods:

  • Proposed an autonomous UV-C disinfection strategy utilizing a dynamic Irradiation Map.
  • The Irradiation Map dynamically records applied UV-C energy per region.
  • Tested the system in various scenarios and compared it against existing disinfection strategies.

Main Results:

  • The autonomous system demonstrated superior performance compared to other disinfection strategies.
  • The dynamic Irradiation Map approach proved effective in optimizing UV-C energy delivery.
  • The method achieved better results, particularly when aiming for high, ideal UV-C doses.

Conclusions:

  • The proposed autonomous UV-C disinfection strategy effectively addresses the need for real-time navigation and disinfection in unknown indoor environments.
  • This approach offers a significant advancement in robotic environmental disinfection, improving safety and efficiency.
  • The dynamic Irradiation Map is a key innovation for achieving targeted and effective UV-C disinfection.