Dynamic data driven methods for self-aware aerospace vehicles

Abstract

A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. Achieving this DDDAS paradigm enables a revolutionary new generation of self-aware aerospace vehicles that can perform missions that are impossible using current design, flight, and mission planning paradigms. To make self-aware aerospace vehicles a reality, fundamentally new algorithms are needed that drive decision-making through dynamic response to uncertain data, while incorporating information from multiple modeling sources and multiple sensor fidelities. In this work, the specific challenge of a vehicle that can dynamically and autonomously sense, plan, and act is considered. The challenge is to achieve each of these tasks in real time-executing online models and exploiting dynamic data streams-while also accounting for uncertainty. We employ a multifidelity approach to inference, prediction and planning-an approach that incorporates information from multiple modeling sources, multiple sensor data sources, and multiple fidelities. © 2012 Published by Elsevier Ltd.