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

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.

Automated summary

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.