Delivering these objectives required us to develop many new experimental techniques, including developing: (i) a state-of-the-art motion capture facility for indoor flight research, with robotic perches, obstacles, and targets; (ii) miniature onboard sensors for outdoor flight research, including lightweight GPS devices with 2 cm accuracy; and (iii) techniques for 3D video reconstruction in the field. In total, we collected quantitative data on over 20,000 flights.
We showed that falcons use the same guidance law as missiles to intercept prey, but that hawks use a “new” guidance law suited to tail-chasing prey through clutter. We then showed that falcons modify their guidance at different stages of an attack when chasing evasive prey, using computer simulation to evolve attack and evasion strategies. This modelling showed that falcons dive after their prey because this allows them to sustain higher loads for manoeuvring. We then showed how the guidance law that hawks use could be implemented visually by measuring the motion of a target against the visual background. Finally, we showed that hawks target fixed points when attacking swarms, which allows them to identify targets without confusion because targets on a collision course appear stationary against a distant background.
We found that the same guidance laws could also be used to model obstacle avoidance, and found that pigeons and zebra finches make use of brightness cues for gap negotiation. We then extended our analyses by using video rendering to visualize what our birds saw as the avoided obstacles. This allowed us to show that birds look at the edges of obstacles, but at the centres of perches. Finally, we combined our studies of pursuit and obstacle avoidance, by putting the two behaviours in conflict. We used this to identify how hawks avoid obstacles during pursuit, using a simple modification of the guidance law that we had previously found.
We challenged our birds to land on moving perches, and found that hawks swoop up to a perch in a way that makes them better able to control their flight in the critical moments before landing. We analysed how they morphed their wings and tail during manoeuvres, identifying a set of control inputs that can be used to describe morphing-wing flight control in birds and air vehicles. These results have applications not only in the design of new morphing-wing air vehicles, but also in the use of machine learning to accomplish tasks like perching.
