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This article shows the potential for unmanned vehicles in general when they are drive by Artificial intelligence (AI) and is a new development in this field.
The article appeared in Smartgear - Oct 8, 2021 and was written by Shane McGlaun . I can see the applicationof AI opening up a whole new field of applications for drones and all nature of unmanned craft.
Researchers at the University of Zürich developed a new AI technique that allows drones to fly autonomously at high speeds through complicated and unknown environments. The drones were able to accomplish their high-speed flights using only onboard sensing and computation. Researchers believe their approach could allow drones to be more useful during an emergency or on construction sites.
The act of navigating an unknown and complicated environment for drones typically requires a map to be given to them beforehand. In these environments, human pilots are typically required for drones to realize their full potential. A new study showed researchers could train an autonomous quadrotor to fly through a complex environment like forests, buildings, rooms, and trains without having seen them before.
The algorithms allowed the drone to operate safely at speeds of up to 40 km/h without crashing into obstacles, trees, or walls.
Researchers used a neural network that learned to fly by watching what the team calls a simulated expert. The simulated expert, in this case, was an algorithm flying a computer-generated drone through simulated environments packed with complex obstacles.
The algorithm was given complete information on the state of the quadrotor and readings from its sensors at all times. It was able to rely on enough time and computational power to find the best trajectory for the given environment. Researchers say their simulated expert provided a considerable advantage over existing AI systems.
Existing systems have to use the sensor data to create a map of the environment before planning their trajectory utilizing the map. That two-step process is time-consuming and makes it impossible for flight at high speed.
After being trained in simulation, the system was tested in the real world, where it was able to fly in a variety of environments without collisions at speeds of up to 40 km/h. “While humans require years to train, the AI, leveraging high-performance simulators, can reach comparable navigation abilities much faster, basically overnight,” says Antonio Loquercio, a PhD student and co-author of the paper. “Interestingly these simulators do not need to be an exact replica of the real world. If using the right approach, even simplistic simulators are sufficient,” adds Elia Kaufmann, another PhD student and co-author.
The applications are not limited to quadrotors. The researchers explain that the same approach could be useful for improving the performance of autonomous cars, or could even open the door to a new way of training AI systems for operations in domains where collecting data is difficult or impossible, for example on other planets.
According to the researchers, the next steps will be to make the drone improve from experience, as well as to develop faster sensors that can provide more information about the environment in a smaller amount of time – thus allowing drones to fly safely even at speeds above 40 km/h.
