Sistema Inteligente de Deteção de Pessoas para Robôs Móveis Autónomos de Desinfeção - Hugo Lima Mendonça (2021)
The COVID-19 virus outbreak led to the need of developing smart disinfection systems, not only to protect the people that usually frequent public spaces but also to protect those who have to subject themselves to the contaminated areas. In this dissertation it is developed a human detector smart sensor for autonomous disinfection mobile robots that uses Ultraviolet C type light for the disinfection task. The disinfection is interrupted by the system when a human is detected around the robot in any direction. UVC light is dangerous for humans and thus the need for a human detection system that will protect them by disabling the disinfection process, as soon as a person is detected. This system uses different sensors, a RGB Raspberry Pi Camera as well as a FLIR Lepton 3.5 Thermal Camera and also four PIR sensors and combines the data gathered by them. The RGB and Thermal images are processed with a Single Shot Detector Mobilenet neural network to identify and detect persons. The thermal camera also generates a temperature histogram for each processed image. Results show that the sensorial fusion of the diffentent sensors data improves the system performance compared to when the sensors are used individually. One of the tests performed proves that the system is able to distinguish a person in a picture from a real person by fusing the thermal camera and the visible light camera data. The detection results validate the proposed system. The system also possesses two extra sensors that communicate directly with the desinfection robot, a LiDAR to measure the area in the detection direction and a sonar to measure the distance from the robot to the ceiling.