According to reports, researchers are developing a drone control system that allows drones to improve the acuity of flying robots by mimicking the trajectory of bicycles and cars crossing the city. After receiving training in urban environments, drones can also Familiar with the environment in areas such as office corridors and parking lots.
According to foreign media reports, researchers have developed a drone control algorithm that allows them to mimic the movement of bicycles and cars through urban areas. Most of the applications of drones appear in open airspace, but it is also a convenient function to move safely through dense urban areas on the streets.
Researchers have proposed a drone control system that allows drones to autonomously pass through these busy areas by showing them the driving modes of cars and cars.
The hustle and bustle of modern cities requires urbanites to pay close attention to mobile obstacles. How to train flying robots? The GPS system used by the drone allowed it to travel in all open spaces above pedestrians, cars, bicycles and walking trails, and researchers at the University of Zurich and the National Research Competence Center trained drones to follow.
This means developing a deep learning algorithm they call DroNet. Instead of using a sophisticated set of sensors to look around the world, the algorithm relies only on conventional cameras like smartphones to safely guide drones around possible obstacles. Teaching DroNet to do this involves collecting a large amount of training data, such as researchers collecting data from bicycles and cars through real-world urban environments. By showing over and over again how DronNet traveled through the streets of the city, it eventually learned how to do this and figure out how to land the drone on the road.
Davide Scaramuzza, professor of robotics and perception at the University of Zurich, said: "This is a computer algorithm that learns to solve complex tasks from a set of 'training samples' that show how drones do something and deal with some difficulties. The situation is like a child learning from a parent or teacher."
The team also found that after receiving training in the urban environment, drones can apply their learning to other environments such as parking lots and office corridors. Antonio Loquercio, a PhD student at DroNet, said: "A lot of technical issues must still be overcome before the most ambitious applications become a reality."
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