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Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing

Daniel Vicente Rodrigo1, J Enrique Sierra-García2, Matilde Santos3

  • 1Complutense University of Madrid, 28040, Madrid, Spain.

Advances in Engineering Software (Barking, London, England : 1992)
|December 5, 2022
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Summary

This study introduces a novel path planning algorithm for mobile robots using UV-C light for automated disinfection. The new approach prevents robot deadlocks, improving disinfection coverage by 58% and ensuring efficient surface sanitation.

Keywords:
Complete coverage path planningDeadlocksEscape routesMobile robotUV-C

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Area of Science:

  • Robotics
  • Disinfection Technology
  • Artificial Intelligence

Background:

  • The COVID-19 pandemic accelerated the need for automated disinfection solutions.
  • Mobile robots equipped with UV-C light offer a promising approach to automated disinfection.
  • Optimizing UV-C disinfection requires balancing complete surface coverage with minimizing radiation exposure.

Purpose of the Study:

  • To develop an optimized Complete Coverage Path Planning (CCPP) algorithm for UV-C disinfection robots.
  • To address challenges in CCPP, including deadlocks and ensuring uniform radiation dosage.
  • To enhance the efficiency and effectiveness of automated UV-C disinfection processes.

Main Methods:

  • A novel architecture combining a Glasius Bio-inspired Neural Network (GBNN), motion strategy, UV-C estimator, speed controller, and pure pursuit controller was designed.
  • A Preventive Deadlock Processing Algorithm (PDPA) and an Escape Route Generator Algorithm (ERGA) were developed to handle CCPP deadlocks.
  • Boustrophedon and spiral motion strategies were compared for their influence on robot navigation performance.

Main Results:

  • The proposed PDPA and ERGA effectively enabled robots to complete complex disinfection maps where GBNN alone was insufficient.
  • A 58% increase in covered surface area was observed with the application of PDPA and ERGA.
  • The study provided insights into the performance differences between boustrophedon and spiral motion strategies in UV-C disinfection navigation.

Conclusions:

  • The developed CCPP algorithm, incorporating PDPA and ERGA, significantly improves the efficiency and coverage of automated UV-C disinfection.
  • The novel architecture effectively addresses the dual challenge of ensuring adequate disinfection and minimizing environmental damage.
  • This research contributes to the advancement of autonomous robotic disinfection systems for public health applications.