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This study introduces a novel UV dose coverage planning framework for autonomous robots, optimizing energy use and reducing redundancy in disinfection tasks. The method enhances robotic disinfection efficiency for pathogen elimination.

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Area of Science:

  • Robotics and Automation
  • Infectious Disease Control
  • Photochemistry

Background:

  • Ultraviolet (UV) radiation is a key strategy for pathogen disinfection.
  • Autonomous mobile robots enhance disinfection coverage but face path planning complexities.
  • High UV dosage for inactivation leads to energy inefficiency and dose redundancy.

Purpose of the Study:

  • To develop an optimized UV dose coverage planning framework for robotic disinfection.
  • To address energy consumption and dose redundancy challenges in large-scale disinfection.
  • To improve the efficiency of autonomous mobile robots in pathogen inactivation.

Main Methods:

  • Utilized a multi-objective optimization model solved with MOPSO (Multi-Objective Particle Swarm Optimization).
  • Integrated a UV-C light efficiency factor into the planning framework.
  • Focused on dose accumulation characteristics for optimized irradiation planning.

Main Results:

  • The proposed framework effectively plans UV dose coverage, mitigating redundancy.
  • Demonstrated significant reduction in energy expenditure for robotic disinfection.
  • Empirical trials confirmed the model's efficacy in deactivating viruses using autonomous robots.

Conclusions:

  • The novel dose coverage planning framework enhances robotic disinfection efficiency and reduces energy consumption.
  • This approach optimizes UV disinfection by prioritizing dose accumulation and light efficiency.
  • The study provides a viable solution for large-scale, energy-efficient robotic disinfection.