Navigation in unknown dynamic environments still remains a major challenge in robotics. Whereas insects like the desert ant with very limited computing and memory capacities solve this task with great efficiency. Thus, the understanding of the underlying neural mechanisms of insect navigation can inform us on how to build simpler yet robust autonomous robots. Based on recent developments in insect neuroethology and cognitive psychology, we propose a method for landmark navigation in dynamic environments. Our method enables the navigator to learn the reliability of landmarks using an expectation reinforcement method. For that end, we implemented a real-time neuronal model based on the Distributed Adaptive Control framework. The results demonstrate that our model is capable of learning the stability of landmarks by reinforcing its expectations. Also, the proposed mechanism allows the navigator to optimally restore its confidence when its expectations are violated. We also perform navigational experiments with real ants to compare with the results of our model. The behavior of the proposed autonomous navigator closely resembles real ant navigational behavior. Moreover, our model explains navigation in dynamic environments as a memory consolidation process, harnessing expectations and their violations.

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.