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This describes a method that could enable the civil use of autonomous aircraft in mission such as airborne package delivery. Establishing such a method greatly strengthens U.S. technology position.
In this talk I will describe NASA strategy and research efforts to provide safety assurance for increasingly autonomous systems used in aviation. In the near future, autonomy will play an important role in civil aviation, and its applications will range from vehicles and platforms (UAVs, transport-class, including supersonic to hypersonic, aircraft) to airspace operations, or health management systems. This infusion of autonomy is driven by a need for optimizing airspace operations to accommodate increasing traffic density (e.g., adaptive trajectory-based operations, autonomous tugs, close parallel runways, and dynamic separation assurance), reducing operation costs to ensure that US operators can compete with emergent countries, and enabling new business models (e.g., fire fighting, UAS-based package delivery and precise aerial photography). In essence virtually every component of the National Airspace System will become increasingly autonomous. Yet we need to do so in a safe manner and have techniques and processes in place to ensure the safety of the public. This talk describes NASA plans to address this problem
This presentation discusses key accomplishments of the MM-RTA project.
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NASAs Armstrong Flight Research Center has been engaged in the development of highly automatic safety systems for aviation since the mid 80s. For the past three years under Seedling and Center Innovation funding this work has moved toward the development of a software architecture applicable to autonomous safety. This work is now broadening and accelerating to address the airworthiness issues surrounding making a case for trustworthy autonomy. This software architecture is called the expandable variable-autonomy architecture (EVAA) and utilizes a run-time assurance approach to safety assurance.
Machine learning is increasingly being used in safety-critical systems, where the public safety requires a rigorous assurance process. We shall outline how assurance processes work for conventional systems and identify the primary difficulty in applying them to machine learning enabled systems. We will then outline a path forward including identifying where considerable basic research remains.
The continued growth of aviation shall require new innovative technologies and operational concepts to meet the ever-increasing demands on air transportation. The NASA Advanced Air Mobility (AAM) project focuses on emerging aviation markets, such as Urban Air Mobility (UAM). UAM is defined as “...a safe and efficient system for air passenger and cargo transportation within an urban area. It is inclusive of small package delivery and other urban unmanned aerial system services and supports a mix of onboard/ground-piloted and increasingly autonomous operations” ([1]). The AAM project emphasizes technology development and validating system-level concepts and solutions in coordination with other NASA Aeronautics Research Mission Directorate (ARMD) projects to enable UAM metro- and micro-plex vertiport and airspace concepts of operations. The NASA AAM research portfolio includes the concepts of Remote Supervisor-in-Command (RSC) and Fleet and Airspace Manager (FAM) as possible human roles for consumer fleet providers. NASA research in RSC is focused on development of guidelines and standards for remote pilots/operators passively and actively controlling a large fleet of autonomous aircraft. For FAM, flight and ground system concepts and technologies to enable high density homogeneous operations at increased scale from vertiport(s), and coordination with other humans in the systems (e.g., UAM urban airspace manager, Air Traffic Control) are key research areas. The envisioned UAM operations are posited to require autonomous systems to enable functions ranging from fleet and resource management to vehicle control. Although automation has become increasingly sophisticated and ubiquitous in civil aviation, autonomy represents a significant evolution in automation, which has generally been limited in functional scope and capability. As autonomy takes on increasing responsibilities, humans and machines will be required to work together in new and different ways [2], rather than traditional design approaches focused on how machines (i.e., autonomy) can do the work of people. The emerging field of human-autonomy teaming (HAT) represents a comprehensive and prioritized research-driven approach to enable the success of future emerging aviation market applications through capabilities and principles that facilitate humans and machine working and thinking better together. The NASA Transformational Tools and Technologies (TTT) Autonomous System (AS) Sub-project was created to assist with the transition into higher levels of autonomy to enable new modes of air transportation, such as UAM. TTT-AS has identified HAT as a key research need to enable UAM while maintaining today’s ultra-safe aviation system safety levels. The latter challenge has been taken up by the NASA System-Wide Safety (SWS) Project, which recognizes that aviation safety, as it evolves, shall require new ways of thinking about safety to include integration of a wide-range of existing and new safety systems and practices, enhanced tools and technologies, increased access to data and data fusion, improved data analysis capabilities, enhanced in-time risk monitoring and detection, hazard prioritization and mitigation, safety assurance decision-support, and in-time integrated system analytics [3].The operational concept of UAM represents a variety of work that has been termed, “work-as-imagined” to characterize the idea that how people think that work is done and how work is actually done are often not the same [4]. To ensure design success and system safety, looking at “work-as-done” provides a comparative approach toward UAM concept and technology design through examination of corresponding analogs found today in aviation (e.g., on-demand operations) and other transportation domains (e.g., port operations). The paper shall discuss various alternative applications with specific focus on airline operation center (AOC) operations, and unmanned aerial system (UAS) command-and-control to inform scaled-versions of FAM and RSC, respectively, and with consideration of the national airspace system contextual environment. The tenets and principles of the HAT field and current NASA research efforts under the TTT-AS sub-project shall also be described. Finally, the SWS sub-project efforts to develop In-Time System-Wide Safety Assurance (ISSA) and In-Time Safety Management Systems (IASMS) are discussed in terms of how “in-time” safety assurance may be conceptualized for the on-demand mobility air taxi “work-as-imagined” operational concept [5]. As part of this effort, concepts from the emerging field of resilience engineering, are being studied. Traditional approaches to aviation safety have focused on what can go wrong and how to prevent it. Another approach to thinking about system safety should reflect not only “avoiding things that go wrong” (protective safety) but also “ensuring that things go right” (productive safety), that enables a system to exhibit the resilient performance [6] necessary for the success of the future aviation system emerging concepts of operations. The paper shall describe efforts focused on how productive safety and resilience may enable a more complete approach to system safety thinking and design of ISSA and IASMS for UAM. Future directions and research needs shall also be discussed.
Advanced capabilities planned for the next generation of autonomous and increasingly autonomous air vehicles will include non-traditional components based on artificial intelligence, machine learning, and complex optimization and planning algorithms. These complex components will be used to provide enhanced safety and high-level decision-making functions. However, there are serious barriers to the deployment of autonomous aircraft in the National Airspace System (NAS). Current civil aviation certification processes are based on the concept that the correct behavior of a system or a component must be completely specified and verified prior to operation. This report from the Autonomy Verification and Validation (V&V) Roadmap and Vision 2045 project presents the most recent effort to build a comprehensive list of verification challenges and needs for autonomous aircraft, a roadmap to meet those autonomy V&V needs, the services they can enable, and point to the certification gaps they fill. To accomplish these goals, we assembled a team of world-class researchers from the aerospace industry (Boeing, Collins Aerospace, and GeneralElectric) and academia (University of Michigan, University of Texas, and Massachusetts Institute of Technology) with deep expertise in autonomy, aerospace systems, and assurance of Artificial Intelligence/machine learning systems.
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Autonomy is an increasingly important technology for robotic scientific and commercial spacecraft. An important motivation for developing onboard autonomy is to enable quick response to dynamic environment and situations, including fault conditions that a spacecraft may encounter. The reliability of such autonomous capabilities depends on the quality of their knowledge of a spacecraft’s health state. Model-based approaches to fault management, i.e. model-based fault diagnosis (MBFD), is one approach to continuously verify correct behavior in addition to diagnosing symptoms to estimate the spacecraft’s health state. The proper functioning of MBFD is dependent on 1) the quality of the model that is analyzed and compared to the outputs of onboard sensors to estimate the system’s health state, and 2) the correct functioning of the diagnosis engine that interrogates the model and compares its analysis to observed system behavior. We are currently developing Verification and Validation (V&V) approaches to provide the necessary confidence that MBFD systems are correctly estimating the health of on-board spacecraft components and systems. Our work is intended to narrow the gap between the rapidly maturing field of ModelBased System Engineering (including MBFD) and the less-well understood area of identifying and applying appropriate V&V techniques to MBFD. Our effort is investigating three areas: 1) developing V&V techniques for the diagnostic model, 2) developing V&V techniques for the diagnostic engine in isolation, and 3) developing V&V techniques for the diagnostic engine and model in combination. This paper describes the work we have completed in the first area. We describe our approach to selecting a system to be represented by a model, the approach to modeling the system, the verification approach we developed for the model, and the results of the verification activity. We conclude with a description of the work remaining in the last two areas, which will be addressed over the next two years.
For the first time in many years, the NASA budget presented to congress calls for a focused effort on the verification and validation (V&V) of complex systems. This is mostly motivated by the results of the VVFCS (V&V of Flight-Critical Systems) study, which should materialize as a a concrete effort under the Aviation Safety program. This talk will present the results of the study, from requirements coming out of discussions with the FAA and the Joint Planning and Development Office (JPDO) to technical plan addressing the issue, and its proposed current and future V&V research agenda, which will be addressed by NASA Ames, Langley, and Dryden as well as external partners through NASA Research Announcements (NRA) calls. This agenda calls for pushing V&V earlier in the life cycle and take advantage of formal methods to increase safety and reduce cost of V&V. I will present the on-going research work (especially the four main technical areas: Safety Assurance, Distributed Systems, Authority and Autonomy, and Software-Intensive Systems), possible extensions, and how VVFCS plans on grounding the research in realistic examples, including an intended V&V test-bench based on an Integrated Modular Avionics (IMA) architecture and hosted by Dryden.
As the US is getting ready for the Next Generation (NextGen) of Air Traffic System, there is a growing concern that the current techniques for verification and validation will not be adequate for the changes to come. The JPDO (in charge of implementing NextGen) has given NASA a mandate to address the problem and it resulted in the formulation of the V&V of Flight-Critical Systems effort. This research effort is divided into four themes: argument-based safety assurance, distributed systems, authority and autonomy, and, software intensive systems. This paper presents an overview of the technologies that will address the problem.
The aviation literature gives relatively little guidance to practitioners about the specifics of architecting systems for safety, particularly the impact of architecture on allocating safety requirements, or the relative ease of system assurance resulting from system or subsystem level architectural choices. As an exemplar, this paper considers common architectural patterns used within traditional aviation systems and explores their safety and safety assurance implications when applied in the context of integrating artificial intelligence (AI) and machine learning (ML) based functionality. Considering safety as an architectural property, we discuss both the allocation of safety requirements and the architectural trade-offs involved early in the design lifecycle. This approach could be extended to other assured properties, similar to safety, such as security. We conclude with a discussion of the safety considerations that emerge in the context of candidate architectural patterns that have been proposed in the recent literature for enabling autonomy capabilities by integrating AI and ML. A recommendation is made for the generation of a property-driven architectural pattern catalogue.
The NASA Resilient Autonomy Project developed a software framework that implemented a Run Time Assurance (RTA) architecture that leveraged ASTM International’s F3269 Industry Standard for safely bounding complex behavior in aircraft. This framework was called the Expandable Variable Autonomy Architecture, or EVAA. EVAA was developed during the height of the Covid-19 lockdown that caused the Resilient Autonomy team to pivot from flight test to distributed simulator testing. EVAA was developed to be platform and mission agnostic where platform specifics were behind a hardware abstraction layer that EVAA called a Coupler. EVAA was able to host multiple safety monitors that could resolve individual safety hazards. EVAA was able to resolve priority conflicts when multiple safety hazards needed to be resolved simultaneously and was able to resolve highly complex situations in a safe manner that could exceed human capabilities.
In early 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) conducted a manned, human-in-the-loop (HITL) simulation. This part-task HITL began the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The results of that sim guided the objectives of the current study, which were to examine pilots’ use of the Airborne Collision Avoidance System (ACAS) rotorcraft variant (Xr) v2 in multiple phases of flight with two separate Xr Modes, fully leverage Xr v2 features (e.g., use radar altimeter data to inform low altitude Resolution Advisory [RA] behavior, utilize the ability to designate “terminal-area intruders,” and display airspeed-based Detect and Avoid [DAA] guidance), emulate a “Traffic Advisory” (TA), and present Xr in a higher-fidelity environment. Therefore, this study was conducted in the Vertical Motion Simulator, and the variables included Phase of Flight (En-route, Hover, and Approach) as well as ACAS Xr Mode (TA/RA and DAA). The data collected included response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability and usability. Additional details and future anticipations are also discussed.
For many decades, researchers at NASA Ames Research Center have worked to make the air-transportation system more efficient, predictable, and effective. Since about 2005 one important aspect of this research has been the development of an autonomous system for air-traffic control. This system, known as the Autoresolver, is designed to perform most of the roles that air-traffic controllers perform including ensuring separation between aircraft, creating routes around weather and other avoidance volumes, and sequencing and scheduling aircraft across points in space. The recent, rapid expansion of new aircraft operations and types, including urban air mobility aircraft and small unmanned aerial systems, have only increased the need for highly automated systems to control the predicted traffic demand. This talk will focus on the development of the Autoresolver - from concept to testing. It will also discuss the National Airspace (NAS) Digital Twin simulation platform, created to facilitate rapid testing and improvement of the algorithm and with the hope of proving the automation in a high-fidelity environment. An open question that will be discussed is how to ensure that the system-level emergent behavior of independently developed autonomous algorithms is what is desired.
This presentation gives a brief overview of how NASA's IV&V Facility approaches assurance for autonomous space systems, and the perceived challenges for the future.
In late 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) conducted a manned, human-in-the-loop (HITL) simulation. This HITL was the lab’s second Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals were to assess detect and avoid technology for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation tested Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the second version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This was conducted at the center's Vertical Motion Simulator (VMS), on a motion-based platform, and was configured to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study included ACAS Xr modes (i.e., TA/RA and DAA) and phases of flight (i.e., Cruise, Hover, and Approach). The data collected included response times, losses of well clear, and pilots' noncompliances to alerts and guidance as well as subjective ratings like acceptability and perceived workload. Additional details and future anticipations are also discussed.