Numerous mobile robots with mounted Ultraviolet-C (UV-C) lamps were developed recently, yet they cannot work in the same space as humans without irradiating them by UV-C. This paper proposes a novel modular and scalable Human-Aware Genetic-based Coverage Path Planning algorithm (GHACPP), that aims to solve the problem of disinfecting of unknown environments by UV-C irradiation and preventing human eyes and skin from being harmed. The proposed genetic-based algorithm alternates between the stages of exploring a new area, generating parts of the resulting disinfection trajectory, called mini-trajectories, and updating the current state around the robot. The system performance in effectiveness and human safety is validated and compared with one of the latest state-of-the-art online coverage path planning algorithms called SimExCoverage-STC. The experimental results confirmed both the high level of safety for humans and the efficiency of the developed algorithm in terms of decrease of path length (by 37.1%), number (39.5%) and size (35.2%) of turns, and time (7.6%) to complete the disinfection task, with a small loss in the percentage of area covered (0.6%), in comparison with the state-of-the-art approach.

翻译：近来，众多配备紫外-C（UV-C）灯管的移动机器人得以研发，但这些机器人无法在有人类共存的空间中工作，以免UV-C辐射对人体造成伤害。本文提出一种新颖的模块化、可扩展的基于遗传算法的人类感知覆盖路径规划算法（GHACPP），旨在解决通过UV-C辐照对未知环境进行消毒、同时防止人类眼睛和皮肤受损的问题。该基于遗传算法的算法在探索新区域、生成消毒轨迹片段（称为微轨迹）以及更新机器人周围当前状态这几个阶段之间交替运行。系统在有效性和人类安全性方面的性能得到验证，并与最新在线覆盖路径规划算法之一SimExCoverage-STC进行了比较。实验结果表明，与当前最优方法相比，所提算法在保障人类高安全性方面表现优异，同时在路径长度（缩短37.1%）、转弯次数（减少39.5%）、转弯幅度（减小35.2%）及完成消毒任务时间（缩短7.6%）方面效率显著提升，覆盖面积百分比仅略有下降（0.6%）。