Understanding visual attention beehind bee-inspired UAV navigation
This work addresses UAV navigation challenges by mimicking bee-inspired strategies, offering a potential simple control law for physical drones, though it is incremental as it builds on existing bio-inspired methods.
The researchers tackled the problem of autonomous UAV navigation by training a Reinforcement Learning agent to navigate a cluttered tunnel using only optic flow as sensory input, finding that agents primarily attend to regions of discontinuity and large magnitude in optic flow, resembling insect behavior.
Bio-inspired design is often used in autonomous UAV navigation due to the capacity of biological systems for flight and obstacle avoidance despite limited sensory and computational capabilities. In particular, honeybees mainly use the sensory input of optic flow, the apparent motion of objects in their visual field, to navigate cluttered environments. In our work, we train a Reinforcement Learning agent to navigate a tunnel with obstacles using only optic flow as sensory input. We inspect the attention patterns of trained agents to determine the regions of optic flow on which they primarily base their motor decisions. We find that agents trained in this way pay most attention to regions of discontinuity in optic flow, as well as regions with large optic flow magnitude. The trained agents appear to navigate a cluttered tunnel by avoiding the obstacles that produce large optic flow, while maintaining a centered position in their environment, which resembles the behavior seen in flying insects. This pattern persists across independently trained agents, which suggests that this could be a good strategy for developing a simple explicit control law for physical UAVs.