Autonomous Visual Navigation A Biologically Inspired Approach

Inspired by the navigational behaviour observed in the animal kingdom and especially the navigational behaviour of the ants, we attempt to simulate it in an artificial environment by implementing different kinds of biomimetic algorithms. Ants navigate themselves by using retinotopic views and try to move in a position to perceive the world in way to look more like they have memorized it. Using this concept, we implement one robust method, "Perfect Memory", which uses the Snapshot model. Perfect Memory is based on the unrealistic assumption of remembering every single snapshot experienced across a training route. After evaluating the performance of this technique and confirming its robustness, we approach the same problem using Artificial Neural Networks (ANNs) as classifiers. This approach has the advantage of providing a holistic representation of the route and the agent does not need to memorize every single snapshot. The basic idea is that we train an ANN to classify whether a view is part of the route or not using the snapshots as training data. We aim to explore and compare the performance between different ANNs classification techniques using as baseline the Perfect Memory.