A Modular Neural Network Approach to Autonomous Navigation

Abstract

In this thesis we present both a novel neurla network paradigm and an approach for solving sensing and control tasks for mobile robots using this neural network paradigm. Real world tasks have driven the evolution of this methodology and its components, and we apply our methodology successfully to two robotic applications. we conclude that for some tasks, our novel modular neural network approach can achieve comparable or better performance than a traditional monolithic neural network in a much reduced training time. we present the MAMMOTH (Modular Architecture Multi-Modality Theory) neural network paradigm, which is both an architectural blueprint and a training system for combining the internal representations of multiple neural networks each of which is trained to recognize different kinds of features. The modules in a MAMMOTH system are designed to provide functional decomposition of a task. That is, each module performs part of the task for a given in put, and the higher levels of the MAMMOTH network combine the results to get a solution; this is different from many modular neural network techniques in which the higher level arbitrates between complete answers provided by the modules. We apply MAMMOTH networks to several tasks, which include vision for the alignment of an aircraft inspection robot, on-road navigation, and cross-country navigation. Through these tasks we see general applicability of MAMMOTH to real world sensing and control tasks. Ultimately, the greatest benefit of MAMMOTH is that for some tasks, low level features can be learned separately and in parallel, speeding the entire training process for a neural system, without losing any performance.