Learning from Bees: Transferring Navigation Behavior in Animals to Robot Control

Autonomous navigation through constrained environments is critical for applications such as mine exploration, disaster recovery, and planetary missions. Despite their limited neural complexity, honeybees exhibit sophisticated navigation behaviors within confined spaces using optic flow - a mechanism that allows perception of surroundings without physical distance measurements. Leveraging this biological principle, we introduce a bio-inspired robotic navigation system driven by neural networks trained on real honeybee flight data. Through behavior cloning, our approach captures the underlying principles of honeybee navigation, focusing on centering, speed control, and obstacle avoidance capabilities. Our system, utilizing optic flow inputs, performs successful real-time obstacle avoidance in both simulated scenarios and real-world robotic implementations. The network architecture employed is computationally efficient and suitable for deployment on resource-constrained robotic platforms. This shows the efficacy of biologically inspired perception strategies. It also highlights the utilization of raw biological data to train a system that directly captures the principles of efficient navigation seen in insects, which can then be deployed onto a robotic platform with simplicity.