Nasirian B, Mehrandezh M, Janabi-Sharifi F
Faculty of Engineering and Applied Science, University of Regina, Regina, SK, Canada.
Robotics, Mechatronics and Automation Laboratory (RMAL), Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada.
Front Robot AI. 2021 Mar 15;8:624333. doi: 10.3389/frobt.2021.624333. eCollection 2021.
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.
医院的有效消毒对于降低新冠病毒向患者和医护人员的传播风险至关重要。自主移动机器人能够及时且经济高效地执行表面消毒任务，同时避免消毒剂与人员直接接触。本文提出了一种端到端的覆盖路径规划技术，该技术可为移动机器人生成一条连续且无碰撞的路径，以覆盖感兴趣的区域。这项工作的目的是通过使用一种新的基于图形的环境表示法找到最优覆盖路径，从而减少消毒任务的完成时间和成本。将结果与其他现有的先进覆盖路径规划方法进行了比较。结果表明，所提出的方法生成的路径总行进距离更短（重叠次数更少）且转弯次数更少。
