Peñacoba Mario, Bayona Eduardo, Sierra-García Jesús Enrique, Santos Matilde
Department of Digitalization, University of Burgos, 09001 Burgos, Spain.
Institute of Knowledge Technology, University Complutense of Madrid, 28040 Madrid, Spain.
Biomimetics (Basel). 2024 Dec 5;9(12):744. doi: 10.3390/biomimetics9120744.
The COVID-19 pandemic highlighted the urgent need for effective surface disinfection solutions, which has led to the use of mobile robots equipped with ultraviolet (UVC) lamps as a promising technology. This study aims to optimize the navigation of differential mobile robots equipped with UVC lamps to ensure maximum efficiency in disinfecting complex environments. Bio-inspired metaheuristic algorithms such as the gazelle optimization algorithm, whale optimization algorithm, bat optimization algorithm, and particle swarm optimization are applied. These algorithms mimic behaviors of biological beings such as the evasive maneuvers of gazelles, the spiral hunting patterns of whales, the echolocation of bats, and the collective behavior of flocks of birds or schools of fish to optimize the robot's trajectory. The optimization process adjusts the robot's coordinates and the time it takes to stops at key points to ensure complete disinfection coverage and minimize the risk of excessive UVC exposure. Experimental results show that the proposed algorithms effectively adapt the robot's trajectory to various environments, avoiding obstacles and providing sufficient UVC radiation exposure to deactivate target microorganisms. This approach demonstrates the flexibility and robustness of these solutions, with potential applications extending beyond COVID-19 to other pathogens such as influenza or bacterial contaminants, by tuning the algorithm parameters. The results highlight the potential of bio-inspired metaheuristic algorithms to improve automatic disinfection and achieve safer and healthier environments.
新冠疫情凸显了对有效表面消毒解决方案的迫切需求，这促使配备紫外线（UVC）灯的移动机器人成为一项很有前景的技术。本研究旨在优化配备UVC灯的差动移动机器人的导航，以确保在复杂环境中进行消毒时效率最大化。应用了受生物启发的元启发式算法，如瞪羚优化算法、鲸鱼优化算法、蝙蝠优化算法和粒子群优化算法。这些算法模仿生物的行为，如瞪羚的躲避动作、鲸鱼的螺旋捕食模式、蝙蝠的回声定位以及鸟群或鱼群的集体行为，以优化机器人的轨迹。优化过程会调整机器人的坐标以及在关键点停留的时间，以确保完全覆盖消毒区域，并将过度暴露于UVC的风险降至最低。实验结果表明，所提出的算法能有效地使机器人的轨迹适应各种环境，避开障碍物，并提供足够的UVC辐射暴露以灭活目标微生物。通过调整算法参数，这种方法展示了这些解决方案的灵活性和鲁棒性，其潜在应用范围不仅限于新冠疫情，还可扩展到其他病原体，如流感或细菌污染物。结果凸显了受生物启发的元启发式算法在改善自动消毒以及实现更安全、更健康环境方面的潜力。
