New Delhi: Researchers from Caltech and Nvidia have developed a control strategy called FALCON (Fourier Adaptive Learning and CONtrol) for autonomous aerial vehicles that uses reinforcement learning to adaptively handle sudden turbulence, and can dynamically adjust over time based on realtime feedback. While FALCON is not the first control strategy for drones to use reinforcement learning, the previous approaches did not attempt to understand how turbulent winds work at a fundamental level. The previous methods depended on maximising reward functions but were not suitable for working with a range of wind conditions and vehicle configurations without retraining, limiting their use in real-world scenarios.
The FALCON strategy relies on the use of periodic waves called sinusoids to represent wind condition signals. These waves provide an approximation of standard wind motions minimising the required computation. Turbulence shows up as changes in the frequency of these waves. The use of the prior knowledge of how turbulence works on a fundamental level simplifies both the training process, and the control of the aircraft during dynamic conditions, with a limited amount of information. The FALCON strategy has been demonstrated at tested at a wind tunnel in Caltech, using a fully equipped airfoil system that represented a UAV. The wingfoil system was outfitted with pressure sensors and control surfaces for making realtime adjustments.
Safer air travel
The FALCON system was able to stabilise itself in the test environment even when subjected to extreme turbulence. A paper describing the findings has been published in NPJ Robotics. One of the coauthors of the paper, Kamyar Azizzadenesheli says, “Advancements in fundamental AI will change the face of the aviation industry, enhancing safety, efficiency, and performance across a range of platforms, including passenger planes, UAVs, and carrier aircraft. These innovations promise to make air travel and operations smarter, safer, and more streamlined.” The tech can potentially avert disasters such as the Singapore Airlines flight in May where over 100 passengers were injured because of extreme turbulence.
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