A neural network based obstacle-navigation animat in a virtual environment

Abstract

Recently, much research has been done on the possibilities of autonomous robots navigating and performing obstacle avoidance. Vision is an important method of gathering information about obstacles and other objects within an environment. A virtual environment was created, in which an animat (virtual organism) navigates using a visual system comparable to that used by biological organisms. A spike-based neural network model was applied to learning. Various applicable rules on network topology were used, and examination was made of how well the animat learnt under varying sizes of visual input layer and intermediate ‘processing’ layer. In addition, three different training regimes were applied and their different merits discussed. It was discovered that increasing layer size in general improved the performance of the animat, provided that the sizes of the input array and the processing arrays correspond. The learning curve of the animat over time was investigated, allowing an optimal training time to be determined. These findings allow an insight into how well such a system can perform and how its design and training may be optimized.