MIT scientists have developed a new system that can allow drones to autonomously navigate through dense environments such as cities, forests and warehouses.
Being able to avoid obstacles while travelling at high speeds is computationally complex, especially for small drones that are limited in how much they can carry onboard for real-time processing.
Many existing approaches rely on intricate maps that aim to tell drones exactly where they are relative to obstacles, which is not particularly practical in real-world settings with unpredictable objects.
If their estimated location is off by even just a small margin, they can easily crash.
The system considers the drones position in the world over time to be uncertain, and accounts for that uncertainty.
"Overly confident maps won't help you if you want drones that can operate at higher speeds in human environments, said Pete Florence, graduate student at MIT.
An approach that is better aware of uncertainty gets us a much higher level of reliability in terms of being able to fly in close quarters and avoid obstacles, said Florence.
NanoMap uses a depth-sensing system to stitch together a series of measurements about the drones immediate surroundings.
This allows it to not only make motion plans for its current field of view, but also anticipate how it should move around in the hidden fields of view that it has already seen.
It's kind of like saving all of the images you've seen of the world as a big tape in your head, said Florence.
For the drone to plan motions, it essentially goes back into time to think individually of all the different places that it was in, he said.
The team's tests demonstrate the impact of uncertainty. For example, if NanoMap was not modelling uncertainty and the drone drifted just per cent per cent away from where it was expected to be, the drone would crash more than once every four flights.
Meanwhile, when it accounted for uncertainty, the crash rate reduced to two per cent.
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