  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.
