An autonomous drone carrying water to help extinguish a wildfire in the Sierra Nevada might encounter swirling Santa Ana winds that threaten to push it off course. Rapidly adapting to these unknown disturbances inflight presents an enormous challenge for the drone’s flight control system.
To help such a drone stay on target, MIT researchers developed a new, machine learning-based adaptive control algorithm that could minimize its deviation from its intended trajectory in the face of unpredictable forces like gusty winds.
Unlike standard approaches, the new technique does not require the person programming the autonomous drone to know anything in advance about the structure of these uncertain disturbances. Instead, the control system’s artificial intelligence model learns all it needs to know from a small amount of observational data collected from 15 minutes of flight time.
Importantly, the technique automatically determines which optimization algorithm it should use to adapt to the disturbances, which improves tracking performance. It chooses the algorithm that best suits the geometry of specific disturbances this drone is facing.
The researchers train their control system to do both things simultaneously using a technique called meta-learning, which teaches the system how to adapt to different types of disturbances.
Taken together, these ingredients enable their adaptive control system to achieve 50 percent less trajectory tracking error than baseline methods in simulations and perform better with new wind speeds it didn’t see during training.
In the future, this adaptive control system could help autonomous drones more efficiently deliver heavy parcels despite strong winds or monitor fire-prone areas of a national park.