With the ability to independently adjust the thrust of each of their four motors, quadcopters are exceptionally agile compared to more traditional aircraft. But in an effort to create an even more maneuverable drone platform, a group of South Korean researchers have studied adding flying squirrel tech to quadcopters. Combined with machine learning, this is said to significantly increase the prototype’s agility in an obstacle course.

Flying squirrels (tribe Pteromyini)) have large skin flaps (patagium) between their wrists and ankles which they use to control their flight when they glide from tree to tree, along with their fluffy squirrel tail. With flights covering up to 90 meters, they also manage to use said tail and patagium to air brake, which prevents them from smacking with bone jarring velocities into a tree trunk.

By taking these principles and adding a similar mechanism to a quadcopter for extending a patagium-like membrane between its rotors, the researchers could develop a new controller (thrust-wing coordination control, TWCC), which manages the extending of the membranes in coordination with thrust from the brushless motors. Rather than relying on trial-and-error to develop the controller algorithms, the researchers trained a recurrent neural network (RNN) which was pre-trained prior to first flights using simulation data followed by supervised learning to refine the model.

During experiments with obstacle avoidance on a test-track, the RNN-based controller worked quite well compared to a regular quadcopter. A disadvantage is of course that the range of these flying squirrel drones is less due to the extra weight and drag, but if one were to make flying drones that will perch on surfaces between dizzying feats of agility in the air, this type of drone tech might just be the ticket.