An insect brain inspired neural network for visual navigation in unstructured environments

In this work, a spiking neural network is developed that acts as a path encoder and processes a sequence of visual scenes of a realistic, highly unstructured environment. It is able to memorize a simulated four-legged robot and then guide it to a given goal. The overall structure, which exploits the functional roles of specific parts of the fruit fly brain, makes it possible to reproduce the same results as other existing structures while largely reducing the overall complexity of the network. This enables the real-time implementation of real robot navigation. The simulation results confirm the performance of the proposed spiking neural network in controlling a four-legged robot Unitree Go2 simulated in Gazebo.