Data from: Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control

Aerial insects exhibit highly agile maneuvers such as sharp braking,
saccades, and body flips under disturbance. In contrast, insect-scale
aerial robots are limited to tracking non-aggressive trajectories with
small body acceleration. This performance gap is contributed by a
combination of low robot inertia, fast dynamics, uncertainty in
flapping-wing aerodynamics, and high susceptibility to environmental
disturbance. Executing highly dynamic maneuvers requires the generation of
aggressive flight trajectories that push against the hardware limit and a
high-rate feedback controller that accounts for model and environmental
uncertainty. Here, through designing a deep-learned robust tube model
predictive controller, we showcase insect-like flight agility and
robustness in a 750-milligram flapping-wing robot. Our model predictive
controller can track aggressive flight trajectories under disturbance. To
achieve a high feedback rate in a compute-constrained real-time system, we
design imitation learning methods to train a two-layer, fully connected
neural network, which resembles an insect flight control architecture
consisting of a central nervous system and motor neurons. Our robot
demonstrates insect-like saccade movements with lateral speed and
acceleration of 197 centimeters per second and 11.7 meters per second
square, representing 447% and 255% improvement over prior results. The
robot can also perform saccade maneuvers under 160 centimeters per second
wind disturbance and large command-to-force mapping errors. Furthermore,
it performs 10 consecutive body flips in 11 seconds - the most challenging
maneuver among sub-gram flyers. These results represent a milestone in
achieving insect-scale flight agility and inspire future investigations on
sensing and compute autonomy.