• Uncovered the functional fovea in a biological vision
The fovea is normally defined as an area on the retina with a higher density of photoreceptors in an animal. In predatory insects, such as the dragonfly, a “functional fovea” is a retinal area in which the prey is actively maintained during a hunt. With analyses of behaviour and neural recordings, we have discovered how “visual receptive fields” selectively overlap within this area to allow insects to predict the movement of their target prey. On-going work aims to generalize this strategy for creating task-specific “artificial foveae”.
• Developed a method to characterize biomechanical constraints underlying the flight control
Any mobile system in the real world, be it an animal or a drone, is constrained by its biomechanics and mode of locomotion. These features determine how steering can be achieved and must be considered for the execution of visual guidance. We have developed an experimental and analysis pipeline to study these features: given appropriate mechanical models, we can now empirically define key steering constraints for flying agents, insects or drones.
• Demonstrated bioinspired object detection as the basis for obstacle negotiation
Bioinspired motion vision techniques, to date, have tended to use optic flow (i.e. wide-field motion) for obstacle detection. However, this strategy is not robust against small, isolated objects. We explored a more generic guidance framework inspired by how dragonflies detect flying prey. Specifically, we use motion information to identify objects as targets or obstacles in-flight. Preliminary obstacle avoidance behaviour has been implemented on a micro drone.
