The COVID-19 pandemic has become a worldwide concern and has motivated the entire scientific community to join efforts to fight it. Studies have shown that SARS-CoV-2 remains viable onsurfaces for days, increasing the chances of human infection. Environmental disinfection is thus an important action to prevent the transmission of the virus. Despite the valuable contribution of the research community to the field of UV-C disinfection by robots, there still lacks a disinfection system that is fully autonomous and computes its trajectory in real-time and in unknown environments. To meet this need, we propose an autonomous UV-C disinfection strategy for indoor environments based on a dynamic Irradiation Map that indicates the amount of energy applied in each region. Our method was tested in different scenarios and compared with other disinfection strategies. Experiments show that our approach delivers better results, especially when targeting high ideal UV-C doses.
The work aimed to use fiducial markers to locate a robot indoors using a simulation environment as a testing tool. An environment with scattered fiducial markers was developed for the robot to identify and be able to recalculate its position with the odometer sensors reference frame. It was observed that this method applied for indoor environments has a low computational cost, caused for the readability of the landmarks, and a high accuracy in robot localization compared to other methods.
Resumo: O trabalho teve por objetivo utilizar marcos fiduciais para localizar um robô em ambientes internos utilizando simulação como ferramenta de testes. Foi desenvolvido um ambiente com marcos fiduciais espalhados para o robô identificar e poder recalcular a sua posição comparado com o referencial dos sensores de odometria. Foi possível observar que este método aplicado para ambientes internos tem um baixo custo computacional, causado pela facilidade de leitura dos marcos, e uma alta precisão na localização do robô em comparação com outros métodos.
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