When it comes to exploring complex and unknown environments such as forests, buildings or caves, drones are hard to beat. They are fast, agile and small, transport payloads and use sensors to get virtually anywhere. But without a highly accurate map, autonomous drones can hardly find their way around an unknown environment. To fully exploit their potential, they currently still need experienced human pilots.
"When manoeuvring a drone, you need to understand the environment in a fraction of a second in order to quickly steer the drone onto collision-free paths," says Prof Davide Scaramuzza, who heads the Robotics and Perception Group at the University of Zurich. "This is very difficult for both humans and machines. Experienced pilots can reach this level after years of training. But machines still struggle with it."
In a recent study, Scaramuzza and his team trained an autonomous quadrocopter to fly at speeds of up to 40 km/h through previously unknown environments such as forests, buildings, ruins or trains without colliding with obstacles of any kind. The drone relies only on the built-in cameras and the calculations of its on-board computer.
AI requires a learning phase. The Zurich researchers implemented this by training the drone's neural network: the missile learned to fly around obstacles by observing a kind of "simulated teacher": an algorithm that flew a computerised drone through a simulated environment full of complex obstacles. The teacher algorithm was aware of the quadrotor's position and its sensors' readings at all times, and had enough computing power to calculate the best trajectory in a fraction of a second.
The data from this simulated teacher is used to teach the neural network how to predict the best trajectory based on the data transmitted by the sensors. This, the Zurich researchers say, is a big advantage over existing systems that first use