Insect-Like mapless navigation based on head direction cells and contextual learning using chemo-visual sensors

We present a novel biomimetic approach to mapless autonomous navigation based on insect neuroethology. We implemented and tested a real-time neuronal model based on the Distributed Adaptive Control framework. The model unifies different aspects of insect navigation and foraging including landmark recognition, chemical search, path integration and optimal memory usage. Consistent with recent findings the model supports navigation using heading direction information, thus precluding the use of global information. We tested our model using a mobile robot performing a foraging task. While foraging for chemical sources in a wind tunnel, the robot memorizes the followed trajectories, using information from landmarks and heading direction accumulators. After foraging, landmark navigation is tested with the odor source turned off. Our results show stability against robot kidnapping and generalization of homing behavior to stable mapless landmark navigation. This demonstrates that allocentric and efficient goal-oriented navigation strategies can be generated by relying on purely local information.