Jak Kirman, Kenneth Basye, Thomas Dean
We present an approach to building hlgh-level control systems for robotics based on Bayesian decision theory. We show how this approach provides a natural and modular way of integrating sensing and planning. We develop a simple solution for a particular problem as an illustration. We examine the cost of using such a model and consider the areas in which abstraction can reduce this cost. We focus on one area, spatial abstraction, and discuss the design issues that arise in choosing a spatial abstraction. Finally, we discuss an abstraction that we have used to solve problems involving robot navigation, and give a detailed account of the mapping from raw sensor data to the abstraction.