Real-Time Safety-Assured Autonomous Aircraft

ABSTRACT:Aurora Flight Sciences, in collaboration with Worcester Polytechnic Institute, proposes the development of the Real-Time Safety-Assured Autonomous Aircraft (RTS3A) system. The overall goal of the proposed work is to develop a modular and flexible flight management system in order to enable condition-aware flight. The RTS3A system incorporates multi-disciplinary, physics-based models and sensor suites to fully functionalize the flight environment of an aircraft with respect to its structural and propulsion capabilities, which allows for optimization of mission execution as well as condition based maintenance. Specifically, the aim is to develop a standalone package representing the RTS3A system incorporating an open architecture that that will ensure modularity and a high degree of flexibility in terms of subsystem interchangeability. This open architecture will establish standardized subsystem classification, interface protocols, data formatting and processing standards, and software design standards for a general condition-aware system. Initial implementation of the RTS3A system on current unmanned systems will be explored and a candidate platform for further development will be identified.BENEFIT:There are three main avenues that Aurora can pursue for commercialization of the RTS3A aircraft: Unmanned Military Aircraft, Manned Military Aircraft, and Commercial Aircraft. The unmanned military aircraft platform would most likely be the first avenue of approach for commercializing this technology. The RTS3A system is comprised of two major sections: the path planning algorithm and the subsystem prognostic health monitoring systems. The path planning algorithm is most applicable to unmanned systems, where the path planner can be used to inform the vehicle flight path. Optimizing the flight path based on real-time updated vehicle heath state constraints will result in risk reduction: maximum mission performance would be ensured given the current capabilities of the vehicle, as well as cost reduction: fuel savings by operating the vehicle at the most efficient operating conditions. For manned flight, the prognostic health monitoring algorithms are of more interest, although the benefits herein are also applicable to unmanned aircraft. The data of the actual state of the various subsystems can be used to inform maintenance schedules, allowing for longer time intervals between vehicle overhauls, and additionally can potentially pinpoint problem areas with higher than normal levels of degradation to further inform the focus of maintenance activities. This will result in additional cost savings over the lifetime of the vehicle, as well as a running history of the subsystem conditions which will give better estimates of remaining useful life.