Ultraviolet type-C irradiation (UV-C) is an effective no-contact disinfection procedure for surfaces and environments to reduce the spread of severe acute respiratory syndrome coron avirus 2 (SARS-CoV-2), the virus that causes COVID-19. This work evaluates the effect of the adoption of mobile robots for UV-C irradiation, compared to conventional disinfection methods based on static UV-C lamps. On-field evaluation was conducted to measure the energy dose delivered by a robot-based moving source of UV-C radiation at different locations in an indoor environment. The effectively released radiation dose was experimentally measured using distributed UV-C-sensitive detectors, considering all of the environmental factors involved. Moreover, this article proposes a novel trajectory planner consisting of a genetic algorithm (GA) that explores the possible trajectories and disinfection outcomes of a robot moving in a tunable artificial potential field (APF) and is capable of maximizing the delivered UV dose based on ambient geometry. The experimental results show that, compared to a conventional trajectory, an optimized one has better performance in terms of both the coverage of the radiated energy in the environment and the time required to complete the disinfection task.

中文翻译：

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具有优化路径规划的UV-C移动机器人：算法设计和现场测量以改善针对SARS-CoV-2的表面消毒

C型紫外线照射（UV-C）是一种有效的表面和环境非接触式消毒程序，可减少引起COVID-19的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的传播。与传统的基于静态UV-C灯的消毒方法相比，这项工作评估了采用移动机器人进行UV-C照射的效果。进行了现场评估，以测量在室内环境中不同位置的基于机器人的UV-C辐射移动源传递的能量剂量。考虑到所涉及的所有环境因素，使用分布式UV-C敏感检测器通过实验测量了有效释放的辐射剂量。而且，本文提出了一种由遗传算法（GA）组成的新型轨迹规划器，该算法探索了在可调人工势场（APF）中移动的机器人的可能轨迹和消毒结果，并能够根据周围的几何形状最大化传递的紫外线剂量。实验结果表明，与常规轨迹相比，优化轨迹在环境中辐射能量的覆盖范围和完成消毒任务所需的时间方面都具有更好的性能。