Introduction

Abstract

The basis for the material in this book centers around research done in an ongoing long-term project which focuses on the development of highly autonomous unmanned aerial vehicle systems.¹ The actual platform which serves as a case study for the research in this book will be described in detail later in this chapter. Before doing that, a brief background of the motivations behind this research will be provided. One of the main research topics in the project is knowledge representation and reasoning and its use in Uav platforms. A very strong constraint has been placed on the nature of research done in the project where theoretical results, to the greatest extent possible, should serve as a basis for tractable reasoning mechanisms for use in a fully deployed autonomous Uav operating under soft real-time constraints associated with the types of mission scenarios envisioned. Considering that much of the work with knowledge representation in this context focuses on application domains where one can only hope for an incomplete characterization of such domains, this methodological constraint has proven to be quite challenging since, in essence, the focus is on tractable approximate and nonmonotonic reasoning systems. As is well known, until recently, nonmonotonic formalisms have had a notorious reputation for lack of tractable and scalable reasoning systems. At an early stage, a decision was made to investigate a number of standard nonmonotonic reasoning approaches and their combination with approximate reasoning techniques based on the use of rough set theory, or at the very least, guided by intuitions from rough set theory. In addition, a decision was also made to deal seriously with the sense/reasoning gap associated with most state-of-the-art robotic systems where it is often the case that high-level reasoning systems are not strongly grounded in the sensory data continually generated by sensor platforms.