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Efficient Coverage Path Planning for Mobile Disinfecting Robots Using Graph-Based Representation of Environment.

B Nasirian¹, M Mehrandezh¹, F Janabi-Sharifi²

¹Faculty of Engineering and Applied Science, University of Regina, Regina, SK, Canada.

Frontiers in Robotics and AI

|April 1, 2021

Summary

This summary is machine-generated.

Autonomous mobile robots can disinfect hospitals efficiently, reducing COVID-19 transmission risk. This study introduces a new path planning method to optimize robot movement, decreasing disinfection time and costs.

Keywords:

autonomous mobile robots coverage path planning deep reinforcement learning disinfection optimization

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Area of Science:

Robotics
Healthcare Technology
Epidemiology

Background:

Hospital disinfection is critical for reducing COVID-19 transmission among patients and staff.
Autonomous mobile robots offer a safe, efficient, and cost-effective solution for surface disinfection.
Current methods may lack optimal path planning, leading to increased task duration and expenses.

Purpose of the Study:

To develop an end-to-end coverage path planning technique for autonomous mobile robots.
To minimize disinfection task completion time and cost in hospital environments.
To generate continuous, uninterrupted, and collision-free paths for robot navigation.

Main Methods:

A novel graph-based representation of the environment for optimal coverage path planning.
Development of an algorithm for generating continuous and collision-free robot paths.
Comparative analysis against existing state-of-the-art coverage path planning approaches.

Main Results:

The proposed technique generates paths with significantly shorter total travelled distance.
The new approach results in fewer overlaps during the disinfection coverage.
A reduction in the number of turns required for robot navigation was observed.

Conclusions:

The developed coverage path planning method enhances the efficiency of autonomous mobile robots for hospital disinfection.
This approach effectively reduces task completion time and associated costs.
The optimized path planning contributes to safer and more effective infection control in healthcare settings.