Journal
FRONTIERS IN ROBOTICS AND AI
Volume 8, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/frobt.2021.624333
Keywords
coverage path planning; disinfection; optimization; deep reinforcement learning; autonomous mobile robots
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada [2017-06930]
- Ryerson Dean of Engineering and Architectural Science Research Fund (DRF)
- NSERC
- MITACS Accelerate
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This paper presents an end-to-end coverage path planning technique for autonomous mobile robots to efficiently perform surface disinfection tasks, reducing the risk of COVID-19 transmission, and completing tasks in shorter time and cost-effectively. The proposed approach generates a path with shorter total travelled distance and fewer turns compared to existing state-of-the-art coverage path planning methods.
The effective disinfection of hospitals is paramount in lowering the COVID-19 transmission risk to both patients and medical personnel. Autonomous mobile robots can perform the surface disinfection task in a timely and cost-effective manner, while preventing the direct contact of disinfecting agents with humans. This paper proposes an end-to-end coverage path planning technique that generates a continuous and uninterrupted collision-free path for a mobile robot to cover an area of interest. The aim of this work is to decrease the disinfection task completion time and cost by finding an optimal coverage path using a new graph-based representation of the environment. The results are compared with other existing state-of-the-art coverage path planning approaches. It is shown that the proposed approach generates a path with shorter total travelled distance (fewer number of overlaps) and smaller number of turns.
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