GHACPP: Genetic-based Human-Aware Coverage Path Planning Algorithm for Autonomous Disinfection Robot

TL;DR

This paper introduces GHACPP, a genetic-based, human-aware coverage path planning algorithm for UV-C disinfection robots that enhances safety and efficiency in unknown environments, outperforming existing methods in key metrics.

Contribution

The paper presents a novel modular genetic algorithm that ensures human safety while optimizing disinfection path efficiency in unknown environments.

Findings

Reduced path length by 37.1%

Decreased number of turns by 39.5%

Lowered disinfection time by 7.6%

Abstract

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.