Robustness of the Infomax Network for View Based Navigation of Long Routes

摘要

Insect inspired navigation strategies have the potential to unlock robotic navigation in power-constrained scenarios as they can function effectively with limited computational resources. One such strategy, familiarity-based navigation, has successfully navigated routes of up to 60m using a single layer neural network trained with an Infomax learning rule in online robotic applications. Here we challenge Infomax to navigate longer routes, investigating the relationship between performance, view size, view acquisition rate and network size. By doing so, we determine the parameters at which Infomax operates effectively and explore the profile with which it fails. We show that effective memorisation of familiar views is possible for longer routes than previously attempted, but that this length decreases for reduced input view dimensions. In the selection of an ideal view acquisition rate, we also show that this must be increased with route length for consistent performance. In investigating the applicability to small, lower-power robots, we demonstrate that computational and memory savings may be made with equivalent performance by reducing the network size. Finally, we investigate the profile with which failure occurs, demonstrating increased confusion occurring across the route as it extends in length. These findings are being used to inform theories of insect navigation and improve practical deployment of view based navigation for long routes.