In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting sur...
In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting sur...
In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting surfaces and purifying the air. This paper focuses on a mobile robot equipped with air purification and UV-C LED disinfection to combat airborne pathogens and environmental pollution. Through the onboard sensors, the proposed algorithm uses gaussian process model to predict the high potential air pollution area in real-time without the requirement of a distributed array of sensors, and it avoids environmental modifications. Then we propose the hybrid artificial potential field (HAPF) by integrating the predicted result with the artificial potential field method, to generate a heuristic path that can navigate the robot to the affected areas with obstacle avoidance. Experimental testing in simulated and real-world environments demonstrates the effectiveness of the proposed approach in rapidly planning purification paths, marking significant progress in automated air purification, and disinfection technology.
Convolutional Neural Networks (CNNs) can achieve excellent computer-assisted diagnosis performance, relying on sufficient annotated training data. Unfortunately, most medical imaging datasets, often collected from var...
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