AI Case Study
Researchers from the University of California San Diego trained gliders to autonomously navigate atmospheric thermals with reinforcement learning
Scientists have not yet discovered how birds find and navigate the thermal plumes that assist them in flying. In trying to understand this, researchers at UC San Diego leveraged reinforcement learning to train gliders to autonomously navigate atmospheric thermals. Their research demonstrates the importance of vertical wind accelerations and roll-wise torques as viable biological cues for soaring birds. The study also provides a framework that can be directly applied to the development of UAVs.
Aerospace And Defence
"In the study, conducted collaboratively with the UC San Diego Division of Biological Sciences, the Salk Institute and the Abdus Salam International Center for Theoretical Physics in Trieste, Italy, the team equipped two-meter wingspan gliders with a flight controller. The device enabled on-board implementation of autonomous flight policies via precise control over bank angle and pitch. A navigational strategy was determined solely from the gliders' pooled experiences collected over several days in the field using exploratory behavioral strategies. The strategies relied on new on-board methods, developed in the course of the research, to accurately estimate the gliders' local vertical wind accelerations and the roll-wise torques, which served as navigational cues.
The scientists' methodology involved estimating the vertical wind acceleration, the vertical wind velocity gradients across the gliders' wings, designing the learning module, learning the thermalling strategy in the field, testing the performance of the learned policy in the field, testing the performance for different wingspans in simulations and estimating the noise in gradient sensing due to atmospheric turbulence."
"Our results highlight the role of vertical wind accelerations and roll-wise torques as effective mechanosensory cues for soaring birds and provide a navigational strategy that is directly applicable to the development of autonomous soaring vehicles."
Design of the learning module. The navigational component of the glider is mod- elled as a Markov decision process
R And D
Core Research And Development
"Soaring birds often rely on ascending thermal plumes (thermals) in the atmosphere as they search for prey or migrate across large distances. The landscape of convective currents is rugged and shifts on timescales of a few minutes as thermals constantly form, disintegrate or are transported away by the wind5,6. How soaring birds find and navigate thermals within this complex landscape is unknown."
"A navigational strategy was determined solely from the glider’s pooled experiences, collected over several days in the field. The strategy relies on on-board methods to accurately estimate the local vertical wind accelerations and the roll-wise torques on the glider, which serve as navigational cues."