By Chris Stonor
NASA has announced this week a new and exciting funding scheme worth USD2.5 million to assist a multi-university team to develop and test safety systems for Advanced Air Mobility (AAM) Vehicles, reports a press release.
The team includes more than a dozen researchers from George Washington University, which is project lead, the University of Texas-Austin and MIT’s Lincoln Lab, as well as Vanderbilt. The Vanderbilt team also includes Marcos Quinones-Grueiro, research scientist at the Institute for Software Integrated Systems. The government partner is NASA’s Aeronautics Research Mission Directorate.
The task is a challenging one with machine learning at its core. Autonomous or self-piloted craft must communicate with each other. They must respond to hazards, from weather to equipment malfunction to “uncooperative” other aircraft to prevent collisions and crashes. And all this must unfold in real time, in defined corridors separate from existing air traffic routes but without continuous air control support on the ground.
NASA alongside the Federal Aviation Administration (FAA), wants to stay ahead of that curve and have air traffic management systems (UTMs) in place when commuters take to the sky.
This USD2.5 million-funded three-year project, aims to develop and test the foundations of safety management for advanced urban air mobility. It is hoped these electric takeoff and landing aircraft (eVTOL), will reduce fossil fuel consumption and traffic congestion.
Cornelius Vanderbilt Professor of Engineering Gautam Biswas, enthused, “This is a very exciting task. A machine-learning algorithm is not like a person. It can only do what it has been trained for. It can analyse a situation better than a human, but it doesn’t have the intuition to deal with unusual situations.”
The project tackles three types of hazards: adverse convective weather, winds and fog; corridor incursion by non-cooperative aircraft; and vehicle and component level degradation and faults. Vanderbilt engineers are focusing on the latter.
The Vanderbilt researchers are highly experienced in tracking performance of components and monitoring degradation, and a key innovation they bring to this work is the idea of using reinforcement learning algorithms for online fault tolerant control.
Biswas continued, “What happens with a fault occurs? How can aircraft keep flying by adjusting its controller? Should it continue or alter its route, or is the situation so bad the vehicle must find a place to land immediately?” Adding, “All of this decision-making has to happen on board.”
The approach, which also supports condition-based maintenance for safe flying, will require a cloud infrastructure as well as to support prognostics, risk evaluation and hazard response functions.
Automated aircraft control is not new. Autonomous systems handle more and more functions in commercial and military flights each year with software trained by system models. The difference with the eVTOL safety management project is that it will be data-driven.
NASA has been testing such aircraft at its research centre in Hampton, Virginia. Combined with its Advanced Air Mobility Campaign, the agency is exploring how different aircraft technologies and configurations perform in an urban environment, and researchers will use this data. The team will then tackle not only what and how a machine learns, but also how to make the leap into “learning as you go” using “reinforcement learning.”
Biswas explained, “All of this will be done in a data-driven manner. That is the interesting thing and the challenge. How rich is your data? If you don’t consider the limitations of your data, you will fail.”
Meanwhile, he and the larger team are advising NASA on extending and operationalising a Concept of Operations or ConOps, for AAM. Included will be separate local and regional corridors for autonomous flights, take-off and landing requirements on the ground and designated areas for passenger transfer.
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