Applying genetic programming to scripted mobile robotics

Abstract

In this thesis, we develop a new language for genetic programming, specifically designed for high-level controller scripting on mobile robots. We then test this language against previous conventions on the Robots Everywhere Antbot platform. We develop a genetic programming framework using Python and the new language, to create corridor-following programs in a simple simulation. Using this framework, we conduct a variety of experiments to find good parameters and techniques that apply to this new language, which can evolve the best controllers. Our results suggest that hierarchical GP using a measure of elitism is likely the best solution, and that the new language is very capable of evolving solutions with a high degree of robustness and generality.