Design Principles for Distributed Context Modeling of Autonomous Systems

Abstract

The use of unmanned aerial vehicles (UAV) has seen a rapid increase due to the advancements in drone technology and the wide range of applications. Their adaptability and versatility make them suitable for a great variety of tasks. To fully realize their potential, an autonomous operation is crucial. For modeling environmental perception (i.e., contextual information) as a key enabler of autonomous operations, guiding principles are needed to support system designers in modeling contextual information for autonomous systems. This article precisely addresses this concern and seeks to establish a set of design principles for the distributed context modeling of autonomous systems, such as autonomous UAVs. This is achieved through a systematic review of the literature and the identification of meta-requirements by leveraging a generic context classification model, which serves as the foundation for deriving the design principles. Subsequently, these design principles undergo evaluation within the context of autonomous UAVs through a use case analysis. The goal of this research is to provide a foundation for the development of autonomous systems that can effectively perceive, interpret, and distribute their context. The design principles can serve as a prescriptive guide for the future development of autonomous systems, ensuring efficient and effective operations.