armd strategic thrust 6: assured autonomy for aviation transformation

1

ARMD Strategic Thrust 6:

Assured Autonomy for Aviation Transformation

Vision and Roadmap

Mark Balin

May 24, 2016

Agenda

2www.nasa.gov

• Why Autonomy is

Important for Aviation

and ARMD

• Vision for the Future

of Autonomy in

Aviation

• Outcomes, Benefits,

and Capabilities

• Roadmap Elements

– Research Challenges

– Advancement

Strategies

– Mission Products

• Summary and Status

Strategic Thrust 6

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Autonomy is the ability to achieve goals while operating independently from external control. (adapted from 2015 NASA OCT Roadmap)

NRC: “Increasingly autonomous” (IA) systems … lie

along the spectrum of system capabilities that begin

with the abilities of current automatic systems, such as

autopiloted and remotely piloted (nonautonomous)

unmanned aircraft, and progress toward the highly

sophisticated systems that would be needed to enable the extreme cases.

(from DoD Unmanned Aircraft Systems Roadmap, 2005)

• Supervision Level

• Goal Specification Level

• World View

• Collaboration Level

• Deliberative Layer Risk Awareness

• Habitual Layer Adaptivity

• Reflexive Layer Quick responsiveness

Endsley and Kaber Automation Levels:1. Manual Control2. Action Support3. Batch Processing4. Shared Control5. Decision Support6. Blended Decision Making7. Rigid System8. Automated Decision Making9. Supervisory Control10. Full Automation

• An “autonomous system” resolves choices on its own.– The goals are provided by another entity

– the system is autonomous from the entity on whose behalf the goals are being achieved

– the decision-making processes may be simple, but the choices are made locally

• An “automated system’ follows a script– albeit a potentially quite sophisticated one

– In unplanned-for situations, it stops and waits for human help, e.g., “phones home.”

– the choices have been made already, and encoded in some way, or will be made externally

(Frost, Chad R.; “Challenges and Opportunities for Autonomous Systems in Space”; 2010)

Autonomy is relative in the sense that it is understood [only] with respect to a specified system – NASA Autonomous Systems Scoping Team

Strategic Thrust 6

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The objective of Strategic Thrust 6 is to enable

autonomous systems that employ highly

intelligent machines to maximize the benefits of

aviation to society.

- NASA Aeronautics Strategic Implementation Plan, 2015

• Emphasis on human/machine continuum over self-governance continuum

• Emphasis on societal benefits

Why Autonomy?

2014 NRC Report*

• The burgeoning industrial sector devoted to the design, manufacture, and sales of [increasing

autonomy] systems is indicative of the perceived economic opportunities that will arise.

• Civil aviation is on the threshold of potentially revolutionary changes in aviation capabilities and

operations associated with IA systems.

Unique Challenges

• Divergent opinions regarding technical feasibility of advanced autonomy

• NRC Report: These systems, however, pose serious unanswered questions about how to safely

integrate these revolutionary technological advances into a well-established, safe, and efficiently

functioning NAS…

• Many non-technical barriers in aviation (economic, socio-cultural, potential for adverse

consequences)

• Traditional approaches to research, development, and implementation in aviation might not

apply to autonomy

• Autonomy may create new markets and value networks, eventually disrupting existing ones and

displacing earlier technologies.

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* Committee on Autonomy Research for Civil Aviation: Aeronautics and Space Engineering Board; Division on Engineering and Physical Sciences; National Research Council: Autonomy Research for Civil Aviation: Toward a New Era of Flight. National Academies Press, 2014.

Vision for the Future of Civil Aviation

• Travelers will have the flexibility to fly when and where they want in a fraction of the time that it takes today

• The skies will accommodate thousands of times the number of vehicles flying today

• There will be a radical increase in new and cost-effective uses of aviation

• All forms of air travel will be as safe as commercial air transport is today

• Aviation will approach overall carbon neutrality

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Autonomy is Required to Enable the Vision

• Anyone can safely fly any time and

anywhere…

• with high confidence…

• in a fraction of the time it takes today…

• while sharing the sky with 1,000 times more

vehicles than today…

• as some of those vehicles accomplish new

missions…

• in close proximity to people and property…

• without harming the environment.

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• Autonomy will foster a radical increase in aviation

efficiency, reliability, and dependability through

system-wide operational planning and highly

responsive replanning to changes

• The aviation system will be so large and complex

that it would be unmanageable without machine

intelligence

• Autonomous machines will achieve unprecedented

agility through high-bandwidth sensing, replanning,

reconfiguration, and control

• Networked multi-vehicle systems will collaborate to

achieve new goals

• Machine intelligence will enable new types of

vehicles and missions, unconstrained by the

requirements of today’s conventional vehicles

• Autonomy will augment human abilities and make

some tasks easier for humans, allowing machines

to assist us and safely work among us

• Configured by autonomous systems, vehicles will

continuously operate at peak performance and

efficiency

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Outcomes, Benefits, and Capabilities

2015 2025 2035

Ou

tco

me

s Introduction of aviation systems with

bounded autonomy, capable of carrying

out function-level goals

Introduction of aviation systems with

flexible autonomy based on earned

levels of trust, capable of carrying out

mission-level goals

Introduction of distributed

collaborative aviation systems with

assured autonomy, capable of

carrying out policy-level goals

Ben

efi

ts

• Efficiency and NAS capacity

• Increased robustness and resilience

in operations

• Enhanced vehicle performance

• Initial UAS applications benefits

• Increased NASA system flexibility,

efficiency and capacity

• Prognostic safety

• New vehicles designed to leverage

autonomy

• Reduced costs at all levels

• Multi-vehicle UAS applications

benefits

• Extreme flexibility and adaptability

for large-scale systems, with

extreme levels of reliability and

recovery from disturbances

• Advanced prognostic safety

• Further reduced costs at all levels

Cap

ab

ilit

ies/

NA

SA

Ou

tpu

ts

• Advanced prescribed automation

and initial goal-directed and adaptive

automation

• Initial world views from local sensors

and limited data exchange

• Applied to aviation system

components and small-scale

systems.

• Predominantly human-supervised;

higher levels of machine

independence under carefully

controlled conditions

• Mission-level goal-directed adaptive

automation

• Large-scale detailed world views

using advanced sensors and

networks

• Applied to large-scale integrated

systems

• Human/machine teams with many

levels of control, depending on

specific situations; extensive

machine-based learning

• Campaign-level goal-directed

adaptive automation, embedded

within all system elements

• Adaptive collaboration based on

extensive shared world views

• Highly distributed large-scale

collaborative systems that

constitute integral parts of larger

systems they support

• Human/machine teams, with

humans primarily specifying

strategic goals; many systems

self-protect and self-heal

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2025 capabilities provide early payoffs and vital steps toward the 2035+ future10-Year Vision for the Future of Civil Aviation

• Anyone can safely

fly…

• any time and

anywhere…

• with high

confidence…

• in a fraction of the

time it takes

today…

• while sharing the

sky with 1,000

times more vehicles

than today…

• as some of those

vehicles

accomplish new

missions…

• in close proximity to

people and

property…

• without harming the

environment.

•Decision aids assist operators with mission replanning and flight guidance. •Onboard systems provide advisories to human operators based on observations, learning, and predictions of what may occur. •Sensing, decision making, and execution systems are capable of assuming control of vehicles to prevent accidents.

•Learning algorithms provide advisories for optimized arrival, departure, and surface operations.•Autonomous scheduling and routing systems enable highly customer-tailorable air transportation.•Natural language processing facilitates communication between humans and machines.

•Onboard systems autonomously monitor, assess, and predict vehicle needs for maintenance or upgrades.•Based on earned levels of trust, intelligent systems perform many tasks previously performed by human operators. •Optimized vehicle design and manufacturing processes reduce certification time and cost.

•Operators and service providers collaborate using intelligent networked systems to continually optimize flight trajectories.•Reliable high-bandwidth low-latency communications enable vehicle-to-vehicle and vehicle-to-ground coordination.

•Advanced learning and data analytics systems balance air traffic demand and airspace system capacity•Remotely operated UAS routinely perform missions in dynamically controlled airspace.•Semi-autonomous UAS traffic management services assure safety of low-altitude UAS operations.

•Teams of UAS, managed by a small number of human operators, operate over large geographical areas.•UAS serving as modules can join to emulate a single vehicle tailored to perform new missions.•UAS autonomously replan or safely end missions in response to changing conditions.

•Onboard systems help to ensure that UAS fly only in approved areas and pose no threat to persons or property.•Semi-autonomous traffic management services ensure that UAS operate within all rules and regulations and mitigate risks related to rogue vehicles. •UAS can act autonomously to avoid collisions with other air vehicles, terrain, and structures.

•Scheduling algorithms generate formation flying opportunities to save fuel and increase route capacity.•Airborne systems automatically maintain air vehicle configurations to optimize flight efficiency.•Autonomous tugs tow aircraft to and from runways, improving efficiency of surface operations.

Roadmap Elements

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Three parallel and interdependent elements to achieve the Vision

Previous Efforts Provide Firm Foundation

for Research Challenges

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NRC Report* Barriers

• Technology barriers

– Communications and data acquisition

– Cyberphysical security

– Decision making by adaptive/

nondeterministic systems

– Diversity of vehicles

– Human-machine integration

– Sensing, perception, and cognition

– System complexity and resilience

– Verification and validation (V&V)

• Regulation and certification barriers

– Airspace access for unmanned aircraft

– Certification process

– Equivalent level of safety

– Trust in adaptive/nondeterministic IA

systems

• Other barriers

– Legal issues

– Social issues

* Autonomy Research for Civil Aviation, 2014

ICAST* Research Themes

• Autonomous planning, scheduling and

decision making

• Real time multi-vehicle cooperation and

interoperability

• Autonomous vehicle control, health

management, adaptation, and optimization

• Human-autonomy teaming

• Secure command and control

• System wide status and assessment

• Autonomy infrastructure and information

management

• Verification, validation, and certification of

autonomous systems

• Test and evaluation capabilities

• Design and analysis of autonomous

systems

* ARMD Inter-Center Autonomy Study Team briefing, 2014

Research Challenges

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Research is organized into five research themes

1. Technologies and Methods for Design of Complex Autonomous Systems

• Methods and technologies for design of intelligent machine systems capable of

operating and collaborating in complex environments

2. Assurance, Verification, and Validation of Autonomous Systems

• Methods for certification and assuring trustworthiness in the design and operation of

autonomous systems

3. Human-Autonomy Teaming in Complex Aviation Systems

• Optimal human-machine role assignments and teaming strategies for increasing

machine autonomy and earned levels of trust

4. Implementation and Integration of Autonomous Airspace and Vehicle

Systems

• Novel real-world autonomy applications and transition paths toward higher levels of

autonomy

5. Testing and Evaluation of Autonomous Systems

• Metrics, models, simulation capabilities, and testbeds for assessment of autonomous

systems in laboratory and operational settings.

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Strategic Thrust 6 Research Challenges

Advancement Strategies

1. Address critical autonomy barriers that require unique NASA

contributions.

2. Leverage initial technologies to insert autonomy into operational

environments, and then build on experience (Evolutionary Autonomy).

3. Develop and demonstrate radical breakthrough autonomy concepts,

technologies, and mission products (Revolutionary Autonomy).

4. Advance autonomy technologies by developing mission products that

leverage the explosive growth and rapid development cycles of

unmanned aerial systems.

5. Leverage large investments in non-aviation autonomy technologies by

repurposing those technologies for aviation.

6. Provide community coordination and leadership to achieve research

advances and implement selected applications.

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Candidate Mission Products

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• Candidate Mission Products: targeted capabilities to be achieved in

Epoch 1 (2015-2025)

– Described in terms of a specific products achievable by 2025

– Provide focus for research and technology development

– Research Themes will apply theme-specific R&D to each mission product

– Provide near-term benefits as well as advancement paths toward ultimate

objectives

• Mission Product selection

– Identified 17 candidate Mission Products based on 2025 Vision, utilizing


– Refinement and down-selection will be based on:

• NASA/community partnership dialogue (in progress)

• NASA goals and resources

• Partnership potential

Candidate Mission Products (1 of 5)

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1. Autonomy-Enabled Airborne Public Safety Services

Goal: Use autonomy technologies to improve safety and effectiveness of airborne public safety

operations

2025 Product: Autonomy-augmented airborne medical services

2. Autonomy-Enabled UAS for Earth Science

Goal: Advance practical civil applications of collaborative UAS swarms

2025 Product: Brassboard capability to support multi-UAS NASA Earth Science missions

3. UAS Traffic Management and Operations

Goal: Safely enable large-scale UAS operations and provide a blueprint for autonomy-centric redesign

of the National Airspace System

2025 Product: Autonomy-based concept and technologies for beyond-visual line-of-sight civil

operations of small UAS

4. Autonomous Airport Surface Operations

Goal: Establish a foundation for in-flight multivehicle collaborative autonomy using ground systems to

enable optimal airport surface operations

2025 Product: Demonstrate an integrated multi-agent autonomous airport ground operations using

initial collaborative autonomy and sensor technologies


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5. Autonomy-Enabled Air Traffic Management

Goal: Advance development of large-scale autonomous decision systems that require integration of

complex constraints and multi-agent coordination, through direct operational experience

2025 Product: Develop a human-supervised autonomous traffic flow management capability based

on large data systems and data analytics

6. Collaborative In-Flight Optimization for Transport Aircraft

Goal: Advance collaborative autonomous air/ground traffic management concepts and technologies

and provide an autonomy modernization path for existing IFR operations

2025 Product: Operational initial autonomy technologies applied to commercial aviation to facilitate

in-flight trajectory optimization, trajectory negotiation, and vehicle teaming

7. Autonomy-Enabled Flight Crew Performance in Complex Environments

Goal: Achieve optimal flight performance, pilot training, and operational resilience through

human/cockpit systems that employ the specialized skills of operators and machine systems

working as an optimized team

2025 Product: Cockpit technology prototypes and guidelines for autonomy-enhanced flight systems,

teamed human/machine decision making, and human/autonomy interfaces to enable

transformational operations and mobility


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8. Autonomy-Enhanced Vehicle Safety

Goal: Make flight vehicles situationally aware of their internal states so they are able to assume an

independent role in safety assurance

2025 Product: Vehicle intelligence technologies that assess vehicle safety state, targeting high risk

scenarios.

9. Resilient, Trusted Autonomous Vehicle Systems

Goal: Achieve safe and reliable operations for autonomous aircraft by integration of resilient vehicle

technologies

2025 Product: A prototype scaled vehicle that demonstrates refuse-to-crash operation in selected off-

nominal conditions through resilient design approaches, ability to deal with uncertainties, and

appropriate response to off-nominal conditions

10. Inflight Vehicle Performance Optimization

Goal: Optimize vehicle performance and efficiency through automated and autonomous systems that

determine the vehicle state and adaptively reconfigure

2025 Product: Demonstration of improved vehicle efficiency with less environmental impact through

intelligent feedback between internal state monitoring and adjustment of vehicle parameters

11. Complex Decision-Making UAS

Goal: Develop autonomous vehicles with reasoning and decision making capabilities to independently

and reliably make safety-related decisions in complex, uncertain environments

2025 Product: Demonstration of UAS that identifies loss of control in selected multiple simultaneous

simulated faults and makes a decision to crash in a manner that does not impact property/personnel


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12. Fully Autonomous Transport Aircraft

Goal: Understand CONOPS, technologies, and system requirements necessary for design,

development, and operations of practical fully autonomous transport class civil aircraft

2025 Product: Design and demonstrate a fully autonomous aircraft to understand the full potential

and costs of autonomy technology for transport class civil aircraft

13. Mission-Adaptive, Eco-Friendly Autonomous Vertical Lift Vehicles

Goal: Design small-scale vertical lift vehicles to be safe, reliable, and eco-friendly through the

application of autonomy technologies, and rapidly advance the state of maturity for autonomy

technologies that improve vehicle design and vehicle operational safety and efficiency

2025 Product: A small-scale autonomous UAV with reliability and performance enhanced and

environmental impacts reduced through use of autonomy-enabled design tools, and operational safety

and utility improved through mission-adaptive health state awareness and prediction technologies

14. Infrastructure for Experimentation, Evaluation, and Testing of Autonomous Systems

Goal: Enable researchers in academia, industry, and government laboratories to provide consistent

benchmarked experimental and evaluation data on autonomous system through well established

methods, tools, and infrastructure

2025 Product: Flexible infrastructure for experimentation, evaluation, and testing of autonomous

systems and multi-agent collaborations


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15. Initial Certification Standards for Autonomous Systems

Goal: Enable certification of autonomous aviation systems and provide verification and validation

(V&V) techniques to support certification

2025 Product: Initial certification standards for runtime assurance and continuous certification

approaches to V&V of autonomous systems, obtained by brokering consensus between regulators

and industry

16. Vehicle Structural Health for Maintenance and Safety

Goal: Integrate distributed structural sensor network data with models of individual vehicles to

determine the structural health and impose necessary constraints on performance or mission

2025 Product: Digital Twin for autonomous sustainment/maintenance and real-time structural safety

in flight vehicles

17. Autonomy-Enable Concepts for Achieving the ATM+3 Vision

Goal: Advance autonomy technologies to enable future National Airspace System densities,

diversities, efficiencies

2025 Product: Stakeholder-vetted autonomy-enabled integrated air/ground concept alternatives that

will enable millions of manned and unmanned platforms to operate in U.S. airspace in a safe and

efficient manner

Candidate Mission Product Linkage to Research Challenges

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Example of Mission Product

Summary Description

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Goal and Benefits• Product: Technology prototypes and procedures for improved

safety and efficiency across a range of public safety operations

• Autonomy Goal: Promote public acceptance of autonomy through

integration of autonomy-enabled capabilities that improve safety and

effectiveness of airborne public safety operations

• Benefits

• Reduced time to reach emergency sites and victims

• Increase in victim lives saved

• Increase in structures/acres saved from fires

• Decrease in relief delivery times (e.g., water, food, medical

supplies)

• Decreased time spent on airborne and ground-based asset

coordination

• Reduced accident rates for airborne public safety personnel

Concepts• Autonomous hazard awareness systems

• Intelligent on-board hazard awareness capability recognizes and

alerts pilots to obstacles, including power lines, trees, intruder

vehicles, etc.; assesses and identifies safe landing zones

• Intelligent decision support

• Pre-flight and real-time in-flight decision support tools facilitate

decisions to go/not go, continue, re-plan, or discontinue the

mission by obtaining and jointly assessing environmental,

mission, aircraft and flight crew parameters

• Small Autonomous Public Safety Vehicles

• Launch from larger airborne (mother ship) or ground-based

assets to provide surveillance and site reconnaissance; and

deliver immediate assistance and emergency supplies; while

independently coordinating with other vehicles and services to

avoid obstacles and assure airspace containment

Current-Day Challenges• Safety

• Helicopter Emergency Medical Services (HEMS) is among the

highest-risk civilian occupations in the US (Blumen, 2009)

• 77% of HEMS accidents weather-related or collisions with

objects

• Intruder drones pose a safety risk to EMS crews and delay

getting victims to healthcare facilities (AAMS)

• Efficiency

• For large-scale disaster relief, ensuring coordination of assisting

assets dramatically slows relief efforts (FEMA, 2005)

Deliverables• Concepts of operation, including policies, standards, regulations,

and procedures

• Prototype systems for autonomy functions

• V&V of flight systems and operations within airspace

• Human-autonomy teaming guidelines and technology

Partner RolesNASA Role

• Identification and resolution of knowledge gaps

• Concepts and prototype technology development

• V&V procedures

Industry Role

• UAS platforms, payload, and nav & control systems

• Sensor and avionics systems for manned aircraft

• Air/air and air/ground communication systems

• Integration of prototype systems

Autonomy-Enabled Airborne Public Safety Services

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Operational View

Summary and Status

• NASA ARMD is developing a roadmap to guide activities for Aeronautics Strategic Thrust 6: Assured Autonomy for Aviation Transformation.

• Major elements of the roadmap are Advancement Strategies, Research Challenges, and Mission Products with outcomes that produce defined capabilities and benefits.

• Due to the game-changing potential and fast-moving nature of autonomous systems, candidate Mission Products are focused primarily on Outcome 1 (2025), with an eye on advancement toward ultimate capabilities.

• The roadmap will evolve based on engagement with the aviation community.

• Execution will rely on collaborative partnerships to strategically leverage efforts and resources.

• The roadmap will be presented at AIAA Aviation 2016 in June, with opportunities for feedback..

• The initial roadmap will be finalized in September 2016.

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Additional Material Mission

Candidate Mission Product Descriptions

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Goal and Benefits• Product: Technology prototypes and procedures for improved

safety and efficiency across a range of public safety operations

• Autonomy Goal: Promote public acceptance of autonomy through

integration of autonomy-enabled capabilities that improve safety and

effectiveness of airborne public safety operations

• Benefits

• Reduced time to reach emergency sites and victims

• Increase in victim lives saved

• Increase in structures/acres saved from fires

• Decrease in relief delivery times (e.g., water, food, medical

supplies)

• Decreased time spent on airborne and ground-based asset

coordination

• Reduced accident rates for airborne public safety personnel

Concepts• Autonomous hazard awareness systems

• Intelligent on-board hazard awareness capability recognizes and

alerts pilots to obstacles, including power lines, trees, intruder

vehicles, etc.; assesses and identifies safe landing zones

• Intelligent decision support

• Pre-flight and real-time in-flight decision support tools facilitate

decisions to go/not go, continue, re-plan, or discontinue the

mission by obtaining and jointly assessing environmental,

mission, aircraft and flight crew parameters

• Small Autonomous Public Safety Vehicles

• Launch from larger airborne (mother ship) or ground-based

assets to provide surveillance and site reconnaissance; and

deliver immediate assistance and emergency supplies; while

independently coordinating with other vehicles and services to

avoid obstacles and assure airspace containment

Current-Day Challenges• Safety

• Helicopter Emergency Medical Services (HEMS) is among the

highest-risk civilian occupations in the US (Blumen, 2009)

• 77% of HEMS accidents weather-related or collisions with

objects

• Intruder drones pose a safety risk to EMS crews and delay

getting victims to healthcare facilities (AAMS)

• Efficiency

• For large-scale disaster relief, ensuring coordination of assisting

assets dramatically slows relief efforts (FEMA, 2005)

Deliverables• Concepts of operation, including policies, standards, regulations,

and procedures

• Prototype systems for autonomy functions

• V&V of flight systems and operations within airspace

• Human-autonomy teaming guidelines and technology

Partner RolesNASA Role

• Identification and resolution of knowledge gaps

• Concepts and prototype technology development

• V&V procedures

Industry Role

• UAS platforms, payload, and nav & control systems

• Sensor and avionics systems for manned aircraft

• Air/air and air/ground communication systems

• Integration of prototype systems


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STRATEGY DESCRIPTION

1a Prototype systems for autonomy functions

1b Verification and validation of flight systems and operations within airspace

1c Human-autonomy teaming guidelines and technology

1d Concepts of operation, including policies, standards, regulations, and procedures for Flight and

Operations within airspace

1e Identify and resolve knowledge gaps through lab and operational evaluations

2a Adaptive automation matures through operational experience

Mixed human/machine operational environment supports advancement of machine intelligence

that teams with humans

2b Direct near-term societal benefit: improved safety and effectiveness for critical public services

4 Vehicles are providers and users of system state data; supports large-scale prognostic data

analytics growth and use

6 Leverage community UAS platforms, payload systems, nav & control systems

Leverage community sensors and avionics for manned systems

Leverage existing air/air & air/ground communication systems

Coordinate with community for integration of prototype systems


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Operational View

2. Autonomy-Enabled UAS for Earth Science

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2025 Mission Product• Product: Testbed and enabling autonomy technologies for a long-

distance, long-duration, pathfinder NASA Earth science mission

using a collaborative, multi-vehicle UAS platform

• Autonomy Goal: Establish the technical foundation for many other

practical civil applications of large-scale collaborative UAS systems

• Benefits

• Increase the quantity and quality of scientific measurements

through a persistent network of airborne sensor platforms

• Fill a gap where satellite coverage is inadequate

• Cost-effective operation of large vehicle swarms over long

distances with minimal supervision

• Momentum of the UAS industry (e.g., atmospheric satellites for

internet access) leveraged toward science missions for public

good

Concept• Large swarms of vehicles provides a persistent, global platform for

continuous scientific measurement

• Vehicle swarm capable of in-situ replanning in response to real-time

observations for autonomous discovery

• Vehicle and sensor payload configuration is heterogeneous and

rapidly customizable for effective mission performance

• Solar-powered, long-endurance, self-managing vehicles with the

ability to land and recharge in the field

• Mission planning and logistics to affordably manage a global

network of vehicles and sensors with minimal supervision

• Rigorous engineering methodology applied to ensure that

autonomous, unsupervised, over-the-horizon operations are safe

and reliable

Current-Day Challenges• Satellites are expensive and relatively inflexible; observations may

have low frequency and inadequate spatial/vertical detail

• UAS require a team of operators (pilot, spotter, mission manager,

etc.) for every single vehicle; a logistics train of special handling

• Technologies for unsupervised navigation and hazard avoidance

are not ready for wide use

• How to assure safety and reliability for autonomous systems is still

an open question

• Building and testing large-scale, multi-vehicle systems in the field is

still difficult or impossible; engineering and regulatory methodology

is in its infancy

Deliverables• Brassboard environment for the development of safe, practical,

integrated mission systems in the field

• Testbed for assured and certifiable, long-range, multi-vehicle

autonomous operations in a real-world environment

• Autonomous technologies for collaborative vehicle swarms

• Vehicle-to-vehicle teaming and adaptive in-flight re-configuration

• Contingency management in response to failures and hazards

• Mission planning and logistics

Partner Roles• NASA and Private Sector: Concepts and Technology

• Science mission customers: tailored requirements for observation

and logistical needs

Autonomy-Enabled UAS for Earth Science

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1a Prototype vehicle swarm behavior for adaptive science observations

1b Testbed for development of assured autonomy for long-distance sensing missions

1c Mission interface and logistics system for long-distance swarm management

1d Concepts of operation, including policies, standards, regulations, and procedures for Flight and

Operations within airspace

1e Testbed for development of assured autonomy for long-distance sensing missions

3 Grand challenge to develop revolutionary airborne sensor network for continuous, affordable,

high-fidelity measurement of the atmosphere and the environment

4 Leverage rapid iteration and momentum of UAS industry toward Earth science missions

6 Leverage community UAS platforms, payload systems, nav & control systems

Coordinate with scientific community to target mission applications of greatest impact

Autonomy-Enabled UAS for Earth Science

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Operational View

3. UAS Traffic Management and Operations

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2025 Mission Product• Product: Autonomy technologies for UAS traffic management, support

services and operations that routinely manage and perform large-scale

visual and autonomous beyond visual line of sight UAS operations across

rural, suburban and urban environments

• Autonomy Goal: Safely enable large scale UAS operations (millions),

safely introduce autonomy-capable and autonomous systems into airspace

operations and provide a blueprint and a path for a complete redesign of

the National Airspace System that utilizes autonomous systems to safely

and efficiently meet the airspace demand of this decade.

• Benefits

• Enables UAS operator flexibility and autonomy

• Safely and securely enables multi-billion dollar UAS industry. An

estimated investment of $82B/decade in drone industry. FAA forecast

shows 2.6M drones by 2020. Provides global leadership opportunity

• Provides a pathway and blueprint for highly scalable, safe and efficient

autonomy-capable beyond NextGen operations

Concept• Application Protocol Interface (API)-based integration of

1) Autonomous Air Navigation Services

2) Autonomous UAS Operations and

3) Autonomous UAS Support Services (USS)

• Intelligent systems optimize UAS operations utilizing autonomous UAS

Support Services and coordinate with autonomous Air Navigation

Services as needed

• Principles of flexibility where possible and structure where needed

enable industry to utilize state of the art autonomous technologies for

UAS operations and UAS support services

• Air Navigation Service Providers utilize secure autonomous information

systems (and promote time critical real-time information exchange) for

safety and security assurance and risk-based management of airspace

operations where airspace operations performance requirements are

dependent on geography and use case with geographical needs,

application, and performance requirements

Current-Day Challenges• Safe, large-scale visual line of sight and autonomous beyond visual

line of sight operations for all types of UAS are not currently possible.

There is a gobal void for concepts, operations requirements, and

technologies and path towards safe large-scale coordinated

operations.

• There is little acceptance of any autonomous vehicle or airspace

operations, but the desired millions of UAS operations can only be

managed through appropriate use of autonomous airborne, ground-

based and cloud-based systems

• There is a huge potential for early targeted autonomy development and

testing in the UAS domain if enabled by adequate airspace integration

constructs

Deliverables• Concepts and architectures for autonomous API based management of

millions of small UAS operations

• Autonomous technologies in research platforms for

• Autonomous ANSP constraint management and airspace

integration

• Autonomous UAS support services that can optimize flight and fleet

operations under multiple constraints and degraded conditions

• Autonomous UAS operations that can meet geography and use

case based performance requirements

Partner Roles• NASA and FAA: Develop primary concepts and architectures

• FAA implements primary ANSP technologies, DoD and DHS and

public safety partners utilize them

• UAS industry develops and implements UAS and UAS support service

technologies to meet performance requirements

UAS Traffic Management and Operations

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1 Breaks critical autonomy acceptance barrier by introducing transparent autonomous air, cloud,

and ground systems for airspace operations. Defines roles and responsibilities of UAS operator,

USS, and ANSPs to maximize scalability

2 Leverages existing and forthcoming UAS technologies developed by industry.

3 Demonstrates autonomous UAS traffic management, services and UAS operations that are not

limited in scalability by human performance limitations

4 Directly advances autonomy technologies and overcomes barriers by developing autonomous

UAS management, services and operational constructs that leverage the high demand for Unmanned Aerial Systems and their rapid development cycles – Enables acceptance of

autonomous vehicle and operator autonomy

5 Leverages large pool of industry partners that have large investments and experience in targeted

or wide-scale autonomy functions from other domains (e.g. internet service providers, road

traffic, geospatial services, cellular providers etc.)

6 Directly and openly involves the FAA and other stakeholders in government, industry and

academia into the research, development and research transition process. Promotes

stakeholder consensus on API and requirements level while leaving flexibility for innovation in

individual airborne, ground-based and cloud-based technologies

UAS Traffic Management and Operations

www.nasa.gov 35

Operational View

Mantra 1: Flexibility where possible and

structure where needed

Mantra 2: Risk based – Geographical

needs, application, and performance-

based airspace operations

5 Key Principles:

• Authenticated operations only

• UAS stay away from each other

• UAS and manned aircraft stay clear

of each other

• UAS operator has awareness of and

stays clear of all constraints

• Public safety UAS have priority

API-based integration of increasingly

• Autonomous UAS Operations

• Autonomous UAS Support Services

• Autonomous Air Navigation Services

4. Autonomous Airport Surface Operations

www.nasa.gov 36

2025 Mission Product• Product: Demonstrate an initial multi-agent approach to integrating

autonomous airport ground operations using initial collaborative

autonomy and sensor technologies

• Autonomy Goal: Establish an initial foundation for in-flight multivehicle

collaborative autonomy using surface vehicles to enable optimal

operations while preventing runway incursions and collision avoidance

• Benefits

• As airports become more congested, autonomous networked

ground vehicles can increase efficiency and safety

• Increased system predictability can improve integrated arrival and

departure operations

• Integration of technologies that can be further developed and tested

for a variety of NASA’s missions

• Reduction in noise and air pollution

Concept• Integrate autonomous ground service vehicles on the airport surface within

the existing airport infrastructure to illustrate feasibility, increasingly efficient

operations, system predictability and robustness

• Supervisory autonomous surface operations will utilize autonomous and

non-autonomous vehicles and algorithms for decision making. Aircraft will

be pulled in using sensor driven pull in guides, autonomous jet bridge

docking, and autonomous push back tugs. Actions would be independently

coordinated amongst all ground vehicles.

• Requirements for vehicle and airport surface sensors to ensure collision

avoidance and continuous operations

• Optimal ground trajectories processes growing volumes of data and

continuously adapting routes under changing conditions

• Automated negotiation between vehicles and prioritization integrated

with arrival and departure scheduling

• Human supervised control and diagnostic monitoring of networked fleet

of autonomous vehicles

Current-Day Challenges• Require greater efficiency and predictability for ground operations:

• In FY15 there were 538,690 arrival and departure delays with an

average taxi out time of 17.30 min (FAA)

• Time and space constraints on current ground services with

increasingly complex logistics

• Costs continue to grow for both airlines and ANSPs

• Increasing opportunities for data-driven, safe, dynamic airport ground

operations to improve efficiency

• ATM autonomy technologies need to be developed and tested to

ensure safety, predictability, and appropriate use

• Certification and ANSP approval challenges require a supervised

autonomy approach

Deliverables• Demonstration of multi-agent autonomous ground service vehicles

integrated within limited autonomous airport surface operations, showing

• Networked vehicles collaborating to enable efficient and safe ground

operations

• Integration of the autonomous vehicles with the ATM infrastructure and

ability to inform and permit autonomous arrival and departure

scheduling algorithms

• Supervisory interface that allows diagnostic monitoring and provides a

testable human/autonomy teaming approach

• Development of a framework for certification and operational approval

Partner Roles• Strongly Leverage Private Sector: Concepts and Technology

• FAA: Achieve requirements for operational approval

• Airlines and Service Providers: Technology assessment and refinement in

collaboration with NASA

Autonomous Airport Surface Operations

www.nasa.gov 37



1 Address critical autonomy barriers that require unique NASA contributions

Use operational data to design and test complex multiagent systems on the airport surface and

test, system assurance, relationships between humans and machines, and system

requirements/standards

Identifies and resolves knowledge gaps through simulations and demonstrations.

Provides a viable path toward large-scale prognostic data analytics growth and use

2 Leverage initial technologies and early adopters to insert autonomy into operational

environments, and then build on operational experience (Evolutionary Autonomy)

5 Leverage large investments in non-aviation autonomy technologies by developing mission

products that repurpose those technologies for aviation where appropriate

Partnership will play a large role in leveraging autonomous vehicle research for sensor

technology, autonomous collaborative algorithms, integrating large quantities of sensor data, and

providing diagnostic reporting capabilities to the user

6 Achieves early technology insertion through mutual-benefit partnerships

Autonomous Airport Surface Operations

www.nasa.gov 38

Operational View

5. Autonomy-Enabled Air Traffic Management

www.nasa.gov 39

2025 Mission Product• Product: Develop initial autonomous TFM advisories using large

data systems and analytics for human-supervised management of

the air traffic system

• Autonomy Goal: Advance development of autonomous decisions

systems and operational concepts using large scale systems

requiring integration of complex constraints for coordinated decision

advisories through direct operational experience

• Benefits

• Ability to more rapidly consider comprehensive decisions using

large datasets and complex constraints

• Improved coordinated decision considering all airports regardless

of size and operations

Concept• Autonomous TFM

• Develop concept and system to develop future autonomous

capabilities.

• Advisories will be developed that can provide timely and

comprehensive decisions regarding traffic flow management

initiatives and autonomous decision making concepts.

• Initially, focus on longer range TFM actions that allow for human

intervention and approval

• Improved data from all aspects of NAS operations

• Aircraft, ground assets, current airspace constraints and demand

along with airports will be instrumented to provide sufficient

information for situational awareness.

• Data will be rapidly accessible and quality checked from a big

data system and made available for supervisory monitoring

Current-Day Challenges• Capacity and Efficiency

• FAA forecasts 16.7 million total operations and 769 million

enplanements in 2030 (FAA)

• In FY15 there were 538,690 arrival and departure delays with an

average taxi out time of 17.30 mi (FAA)

• Need for timely comprehensive multi-objective decisions for this

large collaborative system

• More operations result in complex operations requiring a larger

scope of decision making for an efficient and safe system

• Less frequent strategic decisions in reaction to unexpected

situations reduces system efficiency and capacity

Deliverables• Demonstration of TFM advisories using autonomous algorithms that

uses:

• An autonomy development system that is extensible and

scalable for more comprehensive multi-objective autonomous

decision making and for certification and approval

• A supervisory interface that allows diagnostic monitoring and

provides a testable human/autonomy teaming approach

• A rapidly accessible and comprehensive data repository using

existing and new systems

Partner Roles• Leverage Private Sector: Concepts and Technology

• FAA: Develop system requirements, approval, data sharing, and

operational concept

• Airlines: Technology assessment and refinement in collaboration

with NASA

Autonomy-Enabled Air Traffic Management

www.nasa.gov 40



1a, b Operational trials environment provides data/experience for technical advancements in design of

complex adaptive systems, system assurance, relationships between humans and machines,

and system requirements/standards

1c Provides continually increasing airspace user immersion in machine-assisted collaborative

trajectory management, leading to operator and stakeholder acceptance of new paradigm

1d Keeps certification and operational approval requirements minimal through a supervised

autonomy design approach

1d Provides a viable path toward large-scale prognostic data analytics growth and use

1e Identifies and resolves knowledge gaps through operational evaluations

2a Designed to provide user business cases to support early adoption and continuing growth

5 Leverages existing air/ground connectivity and data analytics to build increasingly rich shared

world view

6 Achieves early technology insertion through mutual-benefit partnerships. Partners: direct

benefits NASA: platform for autonomy advancement through operational experience

Autonomy-Enabled Air Traffic Management

www.nasa.gov 41

Operational View

6. Collaborative In-Flight Optimization for Transport Aircraft

www.nasa.gov 42

2025 Mission Product• Product: Operational initial autonomy technologies that achieve

high benefits in commercial aviation through in-flight trajectory

optimization, trajectory negotiation, and vehicle teaming

• Autonomy Goal: Advance collaborative autonomous air/ground

traffic management concepts and technologies through direct

operational experience, and provide an autonomy modernization

path for existing IFR operations

• Benefits

• Airspace users fly on business-optimal trajectories, continuously

compensating for changing system constraints

• Increased control authority improves system constraint

compliance, leading to increased system predictability

• Multi-vehicle teams save fuel

Concept• Users and airspace resource managers collaborate to achieve

highly efficient operations, system predictability, system robustness,

and resilience to disturbances

• Supported by high-bandwidth air/ground data exchange,

trajectory management automation continuously optimizes routes

under changing conditions

• Automation negotiates between multiple stakeholders with

differing objectives

• On-board sensors, and off-board information systems enable

multi-vehicle rendezvous and teaming for efficiency

• Human operators are supported by increasingly capable automation

and data analytics. Current human roles and authority are

unchanged.

• Adaptive automation is used to increase quality of advisories to

humans over time

• Initial human/automation teaming approach employs machine-

based anticipation of operators’ needs and preferences

Current-Day Challenges• Air/ground trajectory negotiation is recognized as a key enabler for

Trajectory-Based Operations (TBO)

• Missed opportunities for data-driven, safe, dynamic replanning for

efficiency

• For air traffic management applications, autonomy technologies need

to be matured through operational use

• National Airspace System is highly complex; behavior is not

predictable a priori

• Autonomy must be integrated with, and serve the needs of, existing

human operators

• Certification and operational approval challenges require a supervised

autonomy approach

• Initial autonomy implementations may require minimal changes to

existing ATM/ATC processes and procedures

Deliverables• Certified and approved technology and procedures in airborne and

ground-based systems for

• Continuous trajectory optimization based on extensive National

Airspace System (NAS) state knowledge

• Automated negotiation for multi-objective optimization,

accounting for flight, fleet, and service provider goals and

constraints

• Adaptive automation that anticipates and accounts for needs and

goals of human operators

• Technology adopted by NASA partners, who provide operational

data for advanced R&D

Partner Roles• NASA and Private Sector: Concepts and Technology

• Airlines and Service Providers: Technology adoption and refinement

in collaboration with NASA

Collaborative In-Flight Optimization for Transport Aircraft

www.nasa.gov 43



1a, b Operational trials environment provides data/experience for technical advancements in design of

complex adaptive systems, system assurance, relationships between humans and machines,

and system requirements/standards

1c Provides continually increasing airspace user immersion in machine-assisted collaborative

trajectory management, leading to operator and stakeholder acceptance of new paradigm

1d Keeps certification and operational approval requirements minimal through a supervised

autonomy design approach

1d Provides a viable path toward large-scale prognostic data analytics growth and use

1e Identifies and resolves knowledge gaps through operational evaluations

2a Designed to provide user business cases to support early adoption and continuing growth

5 Leverages existing air/ground connectivity and data analytics to build increasingly rich shared

world view

6 Achieves early technology insertion through mutual-benefit partnerships. Partners: direct

benefits NASA: platform for autonomy advancement through operational experience

Collaborative In-Flight Optimization for Transport Aircraft

www.nasa.gov 44

Operational View

7. Autonomy-Enabled Flight Crew Performance in Complex

Environments

www.nasa.gov 45

2025 Mission Product• Product: Revolutionary cockpit technology prototypes and guidelines

for autonomy-enhanced flight systems, teamed human/machine

decision making, and human/autonomy interfaces to enable advanced,

transformational operations and mobility.

• Autonomy Goal: Achieve optimal flight performance, pilot training, and

operational resilience through human/cockpit systems that employ the

specialized skills of operators and machine systems working as an

optimized team.

• Benefits

• Increased pilot/operator/user capability and performance

• Increased safety and efficiency through effective, resilient human-

autonomy teams

• Mitigation of growing pilot shortage

• Enable concepts that improve mobility for people and goods through

a distributed network of autonomy-enhanced aircraft

Concept• Enable advanced transformational operations and mobility through

• Integrated, autonomous situational awareness and self-preservation

capability in response to changes in

• crew state/intent, passenger state, weather, traffic, vehicle

health, and airspace parameters

• Assessment of variable autonomy to support risk management,

decision-making and error mitigation functions

• Approaches to mitigate the pilot as single point of failure due to

• Incapacitation, impairment, human error, malicious behavior

• Assessment of human roles in diverse operational contexts as a

progression from expert crew to operators to users

Current-Day ChallengesIncorporating autonomy into aircraft requires humans and machines “to work

together in new and different ways” (NRC, 2014)

• Safety

• Natural human capacities are increasingly mismatched to the data volumes

and decision speeds demanded in today’s aviation environment

• Today’s Single Pilot Operations are 4-5X more hazardous than 2-crew

operations

• Demand

• Current flight deck procedures and automation are not scalable to

operations in future, higher density NAS (10 to 100x increase in aircraft

traffic density)

• Current systems cannot sustain greater than 3X growth (ODM briefing,

NextGEN)

• Mobility

• Cars and commercial airliners are poorly matched to on-demand regional

travel (50-500 miles)

Deliverables• Guidelines, standards, prototypes and certification methods for revolutionary

cockpits maximizing benefits of crew-autonomy teaming:

• Concept of operations, interface designs, and prototypes for advanced

transformational operation and mobility

• Autonomous crew and vehicle monitoring, fault mitigation, and risk

assessment technologies

• Increasingly autonomous system decision-making and communicate /

execute technologies

• Function allocation analysis to support well-designed pilot-autonomy teams

• Flight demonstration of technology and concepts in a retrofit aircraft with

low-skill pilots

Partner Roles• NASA: Human-autonomy teaming analysis and design

• Industry (esp. GA and bizjet): Integration and revolutionary aircraft and cockpit

designs

• FAA: help establish certification and regulatory framework for highly

autonomous pilot aids

Autonomy-Enabled Flight Crew Performance in Complex

Environments

www.nasa.gov 46



1a Prototype systems for autonomy functions

1b Certification methods for collaborative flight systems

1c Guidelines & technology for dynamic assignment of human/machine roles; human-machine

teaming in normal/non-normal ops.; and for enabling real-time shared understanding between

humans and machine systems

1d Concepts of operation, including policies, standards, regulations, and procedures for high-

density on-demand regional travel

1e Identify and resolve knowledge gaps through lab and operational evaluations.

2a Collaborative human/machine operational environment supports advancement of machine

intelligence that teams with humans

2b Direct near-term societal benefit: improved safety for existing single-pilot operations.

3 Demonstrate control systems for small aircraft eliminating stick-and-rudder piloting in all normal

and non-normal situations

5 Leverage investments in self-driving cars and identify ways to repurpose those advancements

for small aircraft to be used for on-demand regional travel

6 Leverage existing and emerging air/air & air/ground communication systems

Coordinate with community for integration of prototype systems

Autonomy-Enabled Flight Crew Performance in

Complex Environments

www.nasa.gov 47

Operational View

8. Autonomy-Enhanced Vehicle Safety

www.nasa.gov 48

2025 Mission Product• Product: Vehicle intelligence internally assessing safety state, allowing

the vehicle to complete the mission, and improving efficiency while

preventing incidents with on-demand maintenance, targeting high risk

scenarios.

• Autonomy Goal: The vehicle itself is situationally aware of its internal

state, able to respond to both nominal and off-nominal conditions, and

be able, as appropriate, assume an independent role in safety

assurance.

• Benefits

• Vehicles capabilities to independently assure safety may be the only

recourse in some situations, and addresses the recurring issue of

inappropriate crew response.

• Improved situational awareness/response in each vehicle of the

NAS yielding enhanced operations across the NAS.

• Reduced maintenance cost and less vehicle down-time improves

vehicle availability and throughput.

Concept• Future air vehicles, esp. autonomous vehicles, must operate with a

high degree of awareness of their own well-being, and possess the

internal intelligence to provide warning and potentially take action in

response to off-nominal states in order to maintain safety while

performing the mission.

• Networked sensors and algorithms to provide necessary vehicle full-

field state information, fuse and evaluate that information and take

trustworthy real-time or preventive actions.

• Significantly enhance the fidelity and relevance of information provided

to ground systems by the vehicle in-flight for use in on-demand

maintenance.

• The vehicle level intelligence is built from the bottom up with distributed

intelligence and capabilities throughout the vehicle enabling

subsystem/vehicle intelligence

• Vehicle state diagnostics/prognostics that informs decision-making

functions of critical markers trending to unsafe states.

Current-Day Challenges• It is predominately left to pilots (not the vehicle) to interpret current

state and infer future states based on experience and expertise.

• CAST, FAA, NTSB, and the NRC have called for research on systems

that can predict the state of the aircraft, including the state of

autonomous systems, to provide notifications of trending to unsafe

states.

• In order for the trust in autonomy, health management/vehicle

response needs to be tailored for independent autonomous systems

without human intervention

• There has been development in component health management

technology with some adoption; integrated subsystem/vehicle system

full-field health management is limited.

Deliverables• High-priority safety risk cases will define essential state variables

requiring estimation and prediction.

• State awareness technologies for identified vehicle

subsystems/components for nominal and selected off-nominal

operations integrated in a UAS vehicle demonstration.

• Information fusion demonstrated for aircraft subsystems, and an

architecture for vehicle information fusion.

• Diagnostic and prognostic identification of simulated faults before on-

set as part of an enhanced warning system.

• In-flights demonstrations in simulated fault conditions without pilot

intervention.

Partner Roles• Airframers/Engine Companies: Partnership in technology

demonstrations; Feedback on design architecture, technology

implementation, and operational considerations.

• FAA: Involvement in advanced technology demonstrations.

Autonomy-Enhanced Vehicle Safety

www.nasa.gov 49



1a Design of required state variables for vehicle state awareness for high risk scenarios. Methods

for vehicle state awareness, prognostics, and on-demand preventive maintenance

1b Demonstration both in simulations and in-flight of vehicle state awareness, diagnostics, and

prognostic technologies

1d Demonstrates approaches and defines parameters for implementation of vehicle assessment,

mission completion, and early warning systems

1e Methods to evaluate the capabilities of state awareness and early warning vehicle intelligence

through demonstrations and flight test of vehicle safety management

2a Provides a range of technologies and approaches that could be implemented for specific vehicle

applications

2b Identifies methods for identification and early warning of a range of high risk safety scenarios

3 Vehicle independent safety assurance without human intervention demonstrated

5 Leverages information fusion technologies, internet-of-things intelligent sensor development,

data analytics, SA of the environment

6 Industry/FAA to partner in technology demonstrations and evaluations. Provides assessment on

vehicle safety response capability independent of pilot

Autonomy-Enhanced Vehicle Safety

www.nasa.gov 50

Operational View

9. Resilient, Trusted Autonomous Vehicle Systems

www.nasa.gov 51

2025 Mission Product• Product: A prototype scaled vehicle that demonstrates refuse-to-crash

operation in selected off-nominal conditions through resilient design

approaches, ability to deal with uncertainties, and appropriate

response to off-nominal conditions.

• Autonomy Goal: Safe and reliable operations for autonomous aircraft

by integration in design of resilient vehicle technologies and

approaches enabling trust that if an off-nominal state occurs, the

vehicle will assure safety.

• Benefits

• Trust of autonomy and autonomous vehicle systems in the NAS is

enabled by vehicles resilience to off-nominal conditions and

operations with safe and predictable behavior.

• Resilient aircraft systems research has applicability across all

vehicle types and mission classes.

• Semiautonomous aircraft with a high degree of resiliency strongly

impact the overall safety of the NAS.

Concept• Future autonomous air vehicles, flying within new ATM concepts of

operation, must be able to withstand a wide spectrum of uncertain,

abnormal, unexpected, and hazardous conditions (be resilient).

• Autonomous unmanned air vehicles that appropriately respond to

unexpected events in a manner that preserves the safe/efficient

operation of the NAS, as well safeguards people/property.

• Develop integrated systems technologies that enable the mitigation of

multiple hazards, while effectively dealing with uncertainties and

unexpected conditions.

• New vehicles types are being developed and new manufacturing

processes (e.g., additive manufacturing) are being introduced;

Resilient design approaches and built-in intelligence, included in the

design phase.

• Methods and tools for the design, integration, and demonstration of

resilient systems that will lead to improved, assured safety of

operations for new aircraft.

Current-Day Challenges• Future aircraft and operating concepts will depend on unprecedented

levels of autonomy.

• Maintaining current levels of safety for these future vehicles and

operations will require resilience under a wide spectrum of uncertain,

abnormal, unexpected, & hazardous conditions

• Loss-of-Control (LOC) has been identified as the leading causal factor

of aviation accidents (ref: Boeing, NTSB), and is foreseen as such for

the future. Contributing to this is the lack of resilience to the

unexpected, particularly combinations of hazards whose effects are

difficult to understand or mitigate.

• The current status in commercial vehicle operations typically involves

the capability to respond to a limited number of simultaneous hazards

(e.g., control component failures) and vehicle health management that

is applied to specific systems/subsystems/ components after vehicles

have been built and deployed.

Deliverables• Defined resilience traits, metrics, architectures, and mitigation

approaches will be developed with hazards analysis and test scenarios

• Vehicle level fail-safe response technologies including refuse to crash

and mission abort.

• Multi-disciplinary simulations demonstrating required resilient behavior

under a representative set of adverse conditions

• Additive manufacturing applied to fabrication of a resilient scaled

vehicle with integrated intelligence.

• Flight tests of resilient behavior for selected use-cases using a scaled

vehicle.



implementation, and operational considerations


Resilient, Trusted Autonomous Vehicle Systems

www.nasa.gov 52



1a Methods for resilient vehicle manufacturing, operations, and response design

1b Demonstration both in simulations and in-flight of resilient behavior for use cases based on what

is needed for implementation in the airspace

1c The ability to trust autonomous vehicle is core to their implementation in the airspace. A core

objective of this work is to enable trusted behavior

1d Demonstrates approaches for future implementation of independent vehicle operations that

show resiliency to a wide spectrum of operational conditions

1e Identifies knowledge gaps and limits in operational capabilities through design, manufacturing,

and flight test

2a Provides methods to assure vehicle level fail-safe response and mission completion

2b Directly address a major issue in safety related to the ability of vehicles to be resilient to off-

nominal conditions and still safely complete the mission

3 Built-in vehicle intelligence and resilience demonstrated with new approaches to manufacturing

5 Leverages information fusion technologies, data analytics, SA of the environment, and additive

manufacturing advances


vehicle resilience and capabilities to independently achieve mission objectives

Resilient, Trusted Autonomous Vehicle Systems

www.nasa.gov 53

Operational View

10. Inflight Vehicle Performance Optimization

www.nasa.gov 54

2025 Mission Product• Product: Demonstration of improved vehicle efficiency with less

environmental impact through intelligent feedback between internal

state monitoring and adjustment of vehicle parameters.

• Autonomy Goal: Optimize vehicle performance and efficiency with

minimal environmental impact through automated and autonomous

systems that determine the vehicle state and adaptively reconfigure.

• Benefits

• Performance and efficiency optimized by an improved

understanding of real-time vehicle state combined with the

capability optimize operational parameters.

• Reduced environmental impact through intelligent systems that,

e.g., fine-tune operations to decrease emissions or change modes

to reduced noise at airports.

• Independently autonomous vehicles able to not only maintain

safety, but also to fully perform missions without human

intervention.

Concept• Intelligent systems with in-situ vehicle state awareness and ability to in-

flight modify parameters can provide an ability to continuously optimize

operations and reduce environmental impact (as well as provide safety

and resilience).

• Advance inflight state awareness combined with adaptive control highly

to achieve improved efficiency, performance, and reduced

environmental impact.

• Introduction of technologies and approaches from component to

vehicle level needed to allow distributed intelligence enabling an

intelligent vehicle system.

• Integration of autonomy capabilities into advanced air vehicles early in

the design process to enhance or enable improvements in energy

consumption, environmental compatibility, maneuverability and/or

mobility. Demonstrate capabilities to motivate future upgrades and

designs.

Current-Day Challenges• Vehicle operations are often constrained by the adaptability and

capabilities of the pilot/operator, or by static features of the vehicle

design. Some amount of optimization may take place within limited

parameters and in-flight optimization to changing conditions may be

limited.

• Improved efficiency, performance, and environmental impact can be

enabled by increased autonomy and intelligent systems increasing

the parameters considered without overloading the pilot/operator.

• Reduction of environmental impact in other application, e.g.,

automotive or pollution control, is often best achieved with adaptive

system that modify operational parameters.

• Independently autonomous vehicles need the capability to perform

the mission and optimize operations, as well as ensure safety in the

NAS.

Deliverables• Concept of operations for intelligent in-flight vehicle optimization

including adaptation based on mode, stage of mission, and feedback

control.

• Integrated flight/propulsion/vehicle configuration (e.g. active structural

or flow control) to provide optimal mode performance.

• Demonstrate reconfiguration based on the mission stage or vehicle

degradation, e.g., adaptive blade tip clearance control or variable

camber from trailing surfaces.

• Demonstrate feedback control technologies for selected emissions

using on-board measurements/smart fuel injectors.

• Demonstration of these concepts with flight testing in an integrated

vehicle test.



implementation, and operational considerations.

Inflight Vehicle Performance Optimization

www.nasa.gov 55



1a Concept of operations for intelligent vehicle optimization appropriate to the various modes and

stages of vehicle operations. Methods for new inputs and feedback control responses

1b Demonstration of various aspects of the approach in test stand demonstrations as well as the

integrated approach with in-flight testing

1d Demonstrates approaches for implementation of significant advances in vehicle operations

optimization

1e Demonstrates methodology and capabilities through ground and flight tests for improved

efficiency and decreased emissions

2a Provides a range of technologies and approaches that could be implemented for specific vehicle

applications

3 Vehicle independent safety assurance without human intervention demonstrated

5 Leverages a prevalent method for more efficient and greener systems used throughout process

control and automotive industries: automated and autonomous state monitoring and feedback

control

6 Industry/FAA to partner in technology demonstrations to evaluate impact of approaches to

improve efficiency and reduce emissions

Inflight Vehicle Performance Optimization

www.nasa.gov 56

Operational View

A Modern Vehicle’s Capabilities, Including Improved Efficiency and Performance with Reduced

Emissions, Are Enabled by a Complete Vehicle Approach Including Integrated Intelligence

Integrated

flight/propulsion/vehicle

configuration control

Integrated Intelligent Systems

Used to Change Vehicle

Operational Parameters

Reconfiguration based on mission stage

Feedback Emissions

Control

A Smarter Vehicle is a More Efficient and Higher Performance

Vehicle with Less Environmental Impact

“A modern automobile has over 50 microprocessors.” Image and quote at http://www.bu.edu/ece/undergraduate/is-electrical-computer-engineering-right-for-me/automotive-industry/

Ford Model T: https://en.wikipedia.org/wiki/Ford_Model_T

11. Complex Decision-Making UAS

www.nasa.gov 57

2025 Mission Product• Product: A demonstration UAS that determines loss of control in

selected multiple simultaneous simulated faults and makes a

decision to crash in a manner that does not impact

property/personnel.

• Goal: Autonomous and semiautonomous vehicles with reasoning

and decision making capabilities to independently and reliably make

safety-related decisions in complex, uncertain environments.

• Benefits

• Autonomous decision-making is among the most important

properties of a vehicle that has self-governing authority.

• Reliable decision making is crucial to assuring vehicle directed

actions do not result in harm to others.

• Intelligent vehicle-based decision-making removes constraints to

broader use of autonomous vehicles in the NAS.

Concept• Autonomous decision-making is among the most important

properties of a vehicle that has self-governing authority. As vehicles

gain more authority, it must be assured that their decisions do not

result in harm to others or to the vehicle itself if possible.

• Technologies for vehicle-based intelligent decision-making that

enable the core capabilities for vehicles to make independent

decisions.

• Option spaces must be evaluated within time-varying authority

constraints to suggest best course of action to meet both mission &

safety objectives

• Methods for data fusion and information extraction from all available

sources.

• Learning based on prior experiences of the vehicle/other vehicles.

Current-Day Challenges• Current vehicle systems make decisions based on very limited

authority. When exceeded (as in hazardous situations), these

systems disable themselves and it’s often left to pilots to interpret

current state & decide how to mitigate the problem

• To date, vehicle system decision-making authority is very limited

due to reliance on incomplete sensor information, unanticipated

conditions or failure modes, and uncertainty of current and future

states.

• The NRC has noted barriers to the introduction of autonomous

systems including two depending on vehicle cognition: (1) Decision

making by adaptive and nondeterministic systems, and (2) Trust in

increasingly autonomous systems.

• Core to expeditiously and safely modernizing flight operations.

Deliverables• Metrics for quantifying decision performance documented with a

framework for defining constraints.

• Multiple event cases to evaluate decision making approaches.

• Identification of an information acquisition and delivery architecture to

enable decision making.

• Conflict resolution methods in multi-option decision spaces

demonstrated.

• Autonomous decision making methods demonstrated on a UAS with a

series of simulated faults/unexpected events.

Partner Roles• Airframers/Engine Companies/New Service Providers based on UAV’s:

Partnership in technology demonstrations; Feedback on design

architecture, technology implementation, and operational

considerations


Complex Decision-Making UAS

www.nasa.gov 58



1a Addresses one of the most crucial aspects of complex adaptive engineered systems, Decision Making,

through providing metrics, framework for constraints, information acquisition and delivery architecture,

and option spaces

1b Demonstration and evaluation of time-varying authority, conflict resolutions in multi-option decision

spaces, and in-flight decision making with multiple test cases

1c Decision making is core for autonomous operations. This activity emphasizes pushing the limits of

decision making to gain confidence in the capability.

1d Decision making capabilities and approaches are core for future implementation of independent vehicle

systems

1e Evaluation and testing methodologies of a broad range of core aspects of involved in the use of

autonomous systems

2a Provides decision making methods that can be applied to earlier stages of autonomous systems

2b Directly address a major issue in safety related to the implementation of autonomous systems of any

class: if allowed in the airspace, how is the safety of persons/property assured

3 Autonomous systems with this level of machine intelligence needed will enable notable paradigm

changes in NAS operations

4 Core to the use of UAS in the airspace is this core capability for decision making.

5 Leverages machine intelligence, information fusion, data analytics, and SA of the environment


boundaries of decision making systems

Complex Decision-Making UAS

www.nasa.gov 59

Operational View

15

Multiple Problems Occur

at the Same Time:

1. Motor Fault

2. Weather Conditions

3. Heavy Traffic in

Diverse Airspace

X

Make the Right Choice

and Do No Harm

Home and Property Safe Zone

The Mission Will End,

But How?

12. Fully Autonomous Transport Aircraft

www.nasa.gov 60

2025 Mission Product• Product: CONOPS, initial technologies and system requirements

necessary for fully autonomous transport class civil aircraft design,

development and operations in sparse density/infrastructure air space.

• Autonomy Goal: Understand the full benefits and costs of on-board

autonomous technologies impact on transport class civil aircraft

through revolutionary approach of designing a fully autonomous aircraft

for a particular operational niche and advance required autonomous

technologies to enable this.

• Benefits

• Cost effective and appropriately safe civil aircraft configurations

build for a particular mission or market niche.

• Safe, efficient, cost effective transport aircraft designed to be

autonomous from the start drives revolutionary autonomy

technology development, changes system design, configurations,

architectures, and CONOPS with spinoffs to other vehicle

categories e.g. VRLT, ODM,…

Concept• Clean design for fully autonomous transport class aircraft operating in

sparse density/infrastructure.

• Develop CONOPS, architectures, configurations for new missions or

market segments.

• Determine requirements on autonomy technologies for safe and efficient

operations under all conditions and autonomy’s fully impact through

cost/benefit analysis.

• Establish requirements and develop systems and architectures necessary

for fully autonomous transport aircraft with on-board decision making

• Develop adequate intelligent sensing, perception and internal health

monitoring/assessment for required situational awareness (SA).

• Develop on-board intelligent decision-making capability and adaptive

mission management to safely execute defined mission under all

conditions

• Design and build a fully autonomous sub-scale aircraft to access

CONOPS, autonomous system architecture and technologies, and provide

initial data/processes towards certification

Current-Day Challenges• Aircraft design requires pilot cockpit, associated support systems and

CONOPS

• Mission/market specific configuration tailoring is limited small

production numbers not cost effective

• Full impact of autonomous technologies for transport aircraft are

impossible to explore on derivative, cockpit containing configurations

• Transport class aircraft cost and geographic access are limited by

current ATC infrastructure and skilled pilot availability

• Autonomy related technologies are rapidly advancing but require

appropriate aviation application in order to advance across multiple

aviation domains.

• Evolutionary approach to introducing autonomy is unlikely to take full

advantage of autonomous technologies or to produce necessary

certification processes

Deliverables• Initial set of CONOPS and technologies for fully autonomous transport

civil aircraft designed for specific mission/market segment

• CONOPS, autonomous systems architecture and requirements for

safe and efficient operations

• Flight demonstrated enabling autonomy technologies for transport

class aircraft

• Proposed approaches and data for certification for fully autonomous

transport aircraft

• Proof-of-concept configuration design and flight test

• Technology transfer to and technical support for NASA partners for

their mission specific autonomous vehicle applications.

Partner Roles• NASA, Private Sector, DoD: CONOPS, concepts, technology and flight

demonstrator

Fully Autonomous Transport Aircraft

www.nasa.gov 61



1a,b Development of CONOPS and technology requirements, identifying gaps system assurance and

certification

1c Provides immersion in a paradigm-shifting CONOPS for transport aircraft, leading to establishing a

systemic approach to autonomy impact cost/benefit analysis, socialization of new CONOPS and

technologies for airframe manufacturers, users and regulators. Slow, systemic buildup of trust in the

autonomous aircraft

1d Develops system requirements and standards for fully autonomous transport aircraft through spiral

development including design and flight test of autonomous aircraft

1e Identifies technology, tools and methods gaps. Resolves knowledge gaps through design and flight test

3 Embodies the Revolutionary Autonomy strategy:

Clean sheet design-driven; Fully autonomous transport aircraft (no human pilot function) cannot be

done without making use of autonomy capabilities; Drives technical advancements and supports

technology breakthroughs; Finally, advances autonomy without constraints imposed by legacy systems,

legacy infrastructure, regulatory policies and culture.

4 Leverage technology developments for UAS and initial mission/market segment for fully autonomous

transport aircraft would use an appropriate size UAS vehicle

5 Leverages internet-of-things intelligent sensor development, data analytics, SA of the environment

6 Achieves early collaboration on CONOPS, technology development and transfer through mutual-benefit

partnerships. Potential co-funding of autonomy technology demonstrator is of mutual benefit

Provides assessment on full impact of autonomy for transport class aircraft

Fully Autonomous Transport Aircraft

www.nasa.gov 62

Operational View

13. Mission-Adaptive, Eco-Friendly Autonomous

Vertical Lift Vehicles

www.nasa.gov 63

2025 Mission Product• Product: A small-scale autonomous UAV with reliability and performance

enhanced and environmental impacts reduced through use of autonomy-

enabled design tools, and operational safety and utility improved through

mission-adaptive health state awareness and prediction technologies

• Autonomy Goal: Design small-scale Vertical Lift (VL) vehicles as safe,

reliable, and eco-friendly through the application of autonomy technologies,

and rapidly advance the state of maturity for autonomy technologies that

improve vehicle design and vehicle operational safety and efficiency.

• Benefits

• Optimized vehicle performance and efficiency

• Sustained safety and reliability and reduced environmental impacts by

mission adaptive operation.

• Self-reconfigurable power plants responsive to anomalies/faults

enabling safe return to base.

ConceptIntegrated design and on-board health state awareness systems

Development of structural and propulsion system design and manufacturing tools

and models, used to estimate the system life cycle, integrated with on-board

intelligent health-state awareness and regime recognition technologies. The on-

board system will learn as the aircraft interacts with its environment, making

adjustments, to the reconfigurable power and energy management system, to

maintain performance, noise and efficiency based on its mission, operating

environment and component status.

Autonomous integrated flight/propulsion control system

Development of a new autonomous integrated flight/propulsion control system to

maintain optimal power efficiency while adapting to changes in mission from the

on-board intelligent health-state awareness and regime recognition technologies.

Energy management control strategies will be developed that can take advantage

of multiple power sources.

Dynamic system models

Development of analysis capabilities and dynamic models to simulate the

operating systems, including the modeling of realistic anomalies that can help

design and qualify health-state awareness systems, and test their utility in making

maintenance decisions demonstrated by validation and verification performance

metrics.

Current-Day Challenges• Critical VL systems designed to estimated service life in hours –

removed from service before reaching these operational hours.

• Manufacturing defects, aircraft environmental and operational

conditions can cause system degradation.

• Operational data, propulsion control system and health-state data

separately acquired, stored, tracked, trended and monitored.

• Low acceptance of autonomous vehicle in populated areas due to

safety and reliability.

• How to assure safety and reliability for autonomous systems is still an

open question.

• Technologies for autonomous operation are not ready for wide use.

Deliverables• Structural and-propulsion system design tools, dynamic and life

models, health-state awareness, regime recognition algorithms and

autonomous integrated flight/propulsion control system algorithms for

implementation in on-board systems

• Implementation of the technologies into an on-board system

• Demonstration of the on-board system on a UAV

Partner Roles• NASA develops technologies and initial V&V procedures.

• On-board health monitoring system manufacturers integrates the

NASA developed tools with the data acquisitions systems

• UAV designers, manufacturers and operators demonstrate the use and

performance of the tools

• FAA involved from the beginning to aid in certification of systems

Mission-Adaptive, Eco-Friendly Autonomous Vertical

Lift Vehicles

www.nasa.gov 64



1a Prototype autonomous control and health-state awareness systems

1b V & V autonomous health-state awareness and propulsion control systems

1d Qualify health-state awareness systems for maintenance decisions

1e Identify and resolve knowledge gaps through lab and field operation

2a Validate negative environmental impact (noise, emissions)

2b Qualify adaptive system response to failure

3 Demonstrate Mission-adaptive health-state awareness systems

4 UAV technologies for improved reliability and earth friendly

5 Utilize automotive and power generation industries modeling and design tools

6 Leverage community UAS platforms

Mission-Adaptive, Eco-Friendly Autonomous Vertical

Lift Vehicles

www.nasa.gov 65

Operational View

14. Infrastructure for Experimentation, Evaluation, and

Testing of Autonomous Systems

www.nasa.gov 66

2025 Mission Product• Product: Flexible infrastructure for experimentation, evaluation, and

testing of autonomous systems and multi-agent collaborations

• Autonomy Goal: Enable researchers in academia, industry, and

government laboratories to provide consistent benchmarked

experimental and evaluation data on autonomous system through

well established methods, tools, and infrastructure.

• Benefits

• Researchers will have the tools, methods, and infrastructure to

develop, integrate, execute, and analyze meaningful experiments

on autonomous systems.

• Reusable and reconfigurable tools, methods, and infrastructure

will save cost and effort of NASA, academia, and industry

researchers.

• Common tools, methods, and infrastructure will provide

comparable results across researchers, systems, and designs.

Concept• Experimentation with autonomous systems is necessary in order to

understand subtle properties, not-yet-known interactions, emergent

behaviors, and other characteristics of autonomous systems.

• Researchers from academia, industry, and government laboratories

need thorough, versatile, easy-to-use, proven experimental tools,

methods, and infrastructure to provide incontrovertible data and

results.

• Common infrastructure, tools, and methods provide comparable

data results.

• Infrastructure includes instrumented test ranges, HMI test

laboratories.

Current-Day ChallengesThe NRC report identifies “The lack of generally accepted design,

implementation, and test practices for adaptive/nondeterministic systems will

impede the deployment of some advanced IA vehicles and systems in the

NAS?” (p3).

• Infrastructure for Experimentation

• Current infrastructure does not allow for thorough evaluation of

autonomous systems: complex interaction of multiple agents, human-in-

the-loop scenarios, complex failure effects analysis

• Researchers do not have access to comprehensive

experimental/evaluation infrastructure for autonomous systems.

Experiment/evaluation results are not consistent.

• Data sets from different sources are not comparable due to a lack of

consistency in data quality and methods employed.

• Autonomous System Requirements

• Requirements for autonomous systems in aeronautics are not clearly

defined, in part, due to a lack of practical experimental results,

consistent methods, and tools.

Deliverables• Tools, methods, and infrastructure for experimentation and

evaluation of autonomous systems.

Partner Roles• NASA: Develop the tools, methods, and infrastructure; conduct

experiments on limited autonomous systems

• Academia: Provide experimental tools and methods; conduct

experiments on limited autonomous systems

• Industry (airlines, etc.): Conduct experiments on increasingly

complex autonomous systems.

• Other government/regulators: TBD

Infrastructure for Experimentation, Evaluation, and

Testing of Autonomous Systems

www.nasa.gov 67



1 Address critical autonomy barrier by developing flexible infrastructure for experimentation,

evaluation, and testing of autonomous systems and multi-agent collaborations

2 Experiment and evaluate initial technologies in part to develop tools, methods, and infrastructure

to streamline experimentation and evaluation of future technologies

4 Initial focus on the demand for experimentation and evaluation of UAS in relevant environments

will enable a rapid development of capabilities

5 Leverage large investments in non-aviation autonomy technologies by adopting proven tools and

methods, incorporating them into an aeronautic evaluation infrastructure

6 Establish leadership in this area through development of detail project plans and identification of

dedicated workforce and facilities

Identify necessary upgrades to existing laboratories and testing facilities

Identify and develop performance benchmarks and metrics for autonomy impacts

15. Initial Certification Standards for Autonomous Systems

www.nasa.gov 68

2025 Mission Product• Product: Initial certification standards for the safe deployment of

autonomous systems obtained by brokering consensus between regulators

and industry, for systems that utilize runtime assurance and continuous

certification for assurance

• Autonomy Goal: Enabling autonomy certification and providing cost-

effective V&V techniques to support it.

• Benefits

• Confidence that Run-Time-Assurance, Continuous Certification, and

other approaches to VV&C are viable.

• Autonomy researchers will V&V tools/techniques and path toward

certification to deploy their autonomous systems.

• Regulators will have the confidence in limited autonomy civil aviation

systems to grant certification.

Concept• By 2025, we need new V&V methods and Certification standard to enable

the deployment of autonomous systems that can be assured using runtime

assurance and continuous certification

• The assurance overhead and required quick re-certification will limit the

systems that can be handled

• These systems include assured containment systems or systems that

are deployed in environments with no time-critical constraints but with

possible human interactions

• Need a broad consensus (industry & government) for the new proposed

approaches to VV&C autonomous systems, e.g.,

• Run Time Assurance

• Continuous Certification

• Certifiable assured autonomy architectures

• Initial focus on well-defined limited-authority autonomous functions to

enable insight into challenges for larger, more complex systems.

Current-Day ChallengesThe NRC report identifies “one key, crosscutting challenge” to unleashing the

full potential in civil aviation: “How can we assure that advanced IA systems—

especially those systems that rely on adaptive/nondeterministic software—will

enhance rather than diminish the safety and reliability of the NAS?” (pp 32-33).

• VV&C

• Current VV&C methods are not sufficient for self-governing and complex

nondeterministic systems

• Autonomous systems architecture

• Need architectures to effectively enable assurance of safety properties

of autonomous system

• Cooperation

• There is not active consortium dedicated to overcoming the VV&C

barrier of autonomous systems. An industry-government cooperation is

required in order to ensure interoperability, consistent practices and

optimal advancement.

Deliverables• Initial certification standards that can be adopted by FAA to allow the

deployment of autonomous systems with limited functions

• Validated tools and techniques that support the continuous certification of

autonomous systems, e.g., runtime-assurance, continuous certification,

and other recommended practices.

Partner Roles• NASA: act as broker to find solution acceptable by all parties; also develop

supporting V&V toolsets

• FAA, Industry: actively participate in the definition of standards

• Industry: provide help with validating solutions

• Academia: provide insights in autonomy challenges and possible long-term

V&V solutions

Initial Certification Standards for Autonomous Systems

www.nasa.gov 69



1b Develop needed tools & methods for assuring and maintaining trustworthiness and certification

for complex, nondeterministic systems

1c Develop methods to evaluate the viability and impacts of autonomous vehicles and operations

1e Develop metrics, methods and capabilities to assess feasibility, safety, resilience, robustness,

trust, performance, and human interactions with autonomous systems

2b Key enabler to addressing acknowledged aviation safety issues: Identify the system-level and

sub-system level metrics for safety and effectiveness impacts of autonomy

4 Initial focus on the demand for experimentation and evaluation of UAS in relevant environments

will enable a rapid development of capabilities

5 Will lead an industry-government consortium that will leverage investments from non-aviation

autonomy technologies in V&V methods and autonomous-architecture development

6 The NASA-led industry-government consortium will

1. Achieve stakeholder consensus on certification standards

2. Evaluate cost-effectiveness of the proposed enabling V&V toolset

Initial Certification Standards for Autonomous Systems

www.nasa.gov 70

16. Vehicle Structural Health for Maintenance and Safety

www.nasa.gov 71

2025 Mission Product• Product: Digital Twin for autonomous sustainment/maintenance and real-

time structural safety.

• Autonomy Goal: Integration of distributed structural sensor network data

with model of individual vehicle, decision making on the structural vehicle

health and imposition of constraints on performance or mission, and

advancement of required autonomous technologies to enable this.

• Benefits

• Increased flight safety to rarely occurring events by detecting discrete

damage and providing feedback to adaptive mission management on

what flight constraints would prevent overstressing the structure.

• Significant cost reductions, increased aircraft availability, and improved

fleet management

• Eliminate costly general periodic checks (“A-Check” through “D-

Check”)

• Inspect and repair only when problems have been identified for each

vehicle

• Reduced need for maintenance personnel

Concept• Wide-spread vehicle structures sensor networks to monitor critical internal

loads, and structural health/damage characteristics in real-time and during

ground checks

• Individual vehicle informatics containing vehicle manufacturing,

maintenance, and structural health sensor data (obtained both in real time

during flight, and during routine maintenance checks between flights)

• Flight: Real-time, physics-informed models of structural response and

structural margins in response to actual and forecast flight maneuvers

which allows autonomous adaptive mission management software to

maximize vehicle performance while maintaining safety

• Ground: high-fidelity, physics-based models to ascertain structural

degradation and repair requirements during ground maintenance

• Techniques for Data Fusion from sources mentioned above for both real-

time and off-line structural health diagnosis and prognosis

Current-Day Challenges• On-vehicle health monitoring sensors and data acquisition system(s)

• In-process manufacturing sensors and data acquisition system(s)

• Physics-informed and physics-based models of various fidelities for

manufacturing processes, for structural response, and for

material/structural degradation

• Data fusion techniques with uncertainty quantification and reliability

predictions for manufacturing, and for structural margin diagnosis and

prognosis (both real time and between flights)

• Flight control systems that can utilize real-time structural health diagnosis

and prognosis information

• Manufacturing control systems that can use the in-process structural

diagnosis info to autonomously (within accepted bounds) make

manufacturing decisions for part fabrication to requirements

Deliverables• Prototype Digital Twin System with the ability to demonstrate:

• Structural deformation/strain sensor integration and measurement

networking into an on-board data acquisition system

• Sensor/model data fusion to identify discrete source or fatigue damage

and estimate reduction in residual strength/structural margins

• Experimental validation of measurement accuracy and residual

strength/structural margin predictions described above

• Initial integration of Digital Twin System margin data into flight control

system limits

• Prototype initially demonstrated on ground test article, with identification of

and planning for an appropriate flight experiment.

Partner Roles• NASA, Airframe industry, DoD: Ground and flight demonstration

Vehicle Structural Health for Maintenance and Safety

www.nasa.gov 72



1a, b Developing and assessing methods for design, manufacturing and life-cycle sustainment system for

vehicle structural health management system and techniques for its certification

1c Provides immersion in a paradigm-shifting design, manufacturing and maintenance for aircraft, leading

to establishing a systemic approach to autonomous end-to-end individual vehicle structural health

management system (Digital Twin), socialization of new approach and technologies for airframe

manufacturers, users and regulators. Slow, systemic buildup of trust in the highly autonomous aircraft

systems

1d Develops system requirements and standards for autonomous individual vehicle structural health

management system (Digital Twin), through spiral development including design and flight testing of

appropriate experiments

1e Identified technology, tools and method gaps. Resolves knowledge gaps through design and flight test

3 Embodies the Revolutionary Autonomy strategy:

Digital Twin cannot be done without use of autonomy capabilities; Drives technical advancements and

supports technology breakthroughs; Finally, advances autonomy without constraints imposed by legacy

systems, legacy infrastructure, regulatory policies and culture

5 Leverages internet-of-things intelligent sensor development, data analytics, advances in materials and

manufacturing

6 Achieves early collaboration on design, manufacturing and maintenance CONOPS, technology

development and transfer through mutual-benefit partnerships. Potential co-funding of autonomy

technology demonstrator is of mutual benefit

Vehicle Structural Health for Maintenance and Safety

www.nasa.gov 73

Operational View

17. Autonomy-Enabled Concepts for Achieving the

ATM +3 Vision

www.nasa.gov 74

Goal and Benefits• Product: Stakeholder vetted autonomy-enabled integrated

air/ground concept alternatives that will enable millions of manned

and unmanned platforms operating in the US airspace in a safe and

efficient manner

• Develop integrated air/ground autonomy-capable concept

alternatives and enabling autonomy technologies

• Develop models, conduct initial benefits and feasibilities

assessments

• Autonomy Goal: Advance autonomy technologies to enable future

National Airspace System densities, diversities, efficiencies while

maintaining higher affordability and competitiveness

• Benefits

• Enable future densities, operator flexibilities while maintaining

system efficiencies, safety and affordability for all stakeholders

Concept• Reverse the paradigm of control – shift responsibilities towards

operators and third party service providers for planning under

constraints

• Reliance on machine learning based concepts and technologies to

continuously improve with reduced limitation based on human

workload

• Flexibility where possible and structure where necessary,

integration where possible and segregation where necessary

• Leverage common application protocol interface, internet based

systems, cloud-based architecture, machine learning, and

connected systems to enable operator autonomy

Current-Day Challenges• While maintaining priorities of safe, expeditious and efficient flows;

operator efficiency suffers which results in delays or excessive fuel

burn

• Current roles and responsibilities put Air Navigation Service Provider

in the middle of constraint management and tactical trajectory

changes; these human-centric operations limit the scalability

• These problems will only become worse as new entrants, and their

volume, in airspace will continue to grow – traditional aircraft,

personal aircraft vehicles, unmanned aircraft vehicles, commercial

space launches, supersonic/hypersonic vehicles, internet balloons,

wind turbines, etc.

• An entire new paradigm is needed where higher densities and

diverse users can be accommodated without human workload

bottlenecks

Deliverables• Integrated suite of air/platform and ground justifiable autonomy-capable

concepts and technologies that offer strong scalability and affordability

benefits as well as early feasibility – with clear roles and responsibilities for

users, operation centers, platforms, and service providers

• Demonstration of concepts and technologies in simulation – integrated

airline operations, air platform, and service provider capabilities

• Stakeholder vetted justifiable autonomy-capable concept alternatives and

human-autonomy teaming alternatives

Partner Roles• NASA and Private Sector: concepts, architectures, technologies, platforms/

vehicles, simulation environment, models

• Airlines: Data and subject matter expertise

• Platforms: Range of small to large vehicle models

• Service Providers: Technology adoption and refinement in collaboration

with NASA, subject matter expertise

Back-Up

www.nasa.gov 75

Key Reference Documents

1. National Research Council: Autonomy Research for Civil Aviation:

Toward a New Era of Flight

2. ICAST: Recommendations on NASA’s Civil Aviation Autonomy Research

Strategy

3. AIAA Intelligent Systems Technical Committee: Roadmap for Intelligent

Systems in Aerospace (Draft)

4. Automax Workshops Proceedings (Draft TM)

5. LaRC Autonomous Systems Brief to ARMD – Updated

6. Defense Science Board: The Role of Autonomy in DoD Systems

7. MITRE: Anticipating the Onset of Autonomy: A Survey of the DoD,

Armed Service, and other Federal Agencies’ Outlook on Autonomy

www.nasa.gov 76

Additional Literature Sources (continued)

Major Research Needs Cited in Multiple Documents

• Need for improved tools, design standards, models, simulations, and architectures, including

• Common representations and architectures that facilitate interaction between intelligent

systems and humans

• Standards in design and analysis methods

• Methods and tools assisting in verifiable requirements development and analysis

• Modeling learning, reasoning, perception, and smart behaviors

• Models for sensing, perception, cognition, and intelligent decision making

• Understanding of human-agent interactions

• Human/unmanned systems with scalable and robust distributed collaboration

• Human-machine communication

• Ensuring robustness, trust and assurance

• Unpredictable environments and interactions with other systems can lead to unexpected

emergent behaviors

• Reliance on human operator to compensate for brittleness

• Developing new approaches to certification

• Test, Evaluation, Verification, and Validation

• Testing and assessing safety and performance of complex of self-learning autonomous systems

www.nasa.gov 77


Strategy 1

Address critical autonomy barriers that require unique NASA contributions

and leadership

a. Design and behavior of complex adaptive engineered systems

b. System assurance and certification

c. Relationships between humans and machines, including operator and

societal trust

d. System requirements and standards to facilitate integration and

implementation

e. Methods and capabilities to test and evaluate autonomous systems

Research Themes and OTCs embody Strategy 1

www.nasa.gov 78


Strategies 2 and 3

Parallel Autonomy Advancement Paths

Strategy 2: Evolutionary Autonomy

• Provide incremental benefits by inserting advanced technology into existing

systems

• Gain confidence and refine capabilities from technology insertion experiences

• Specific Objectives:

a) Provide early direct benefits to users

b) Address acknowledged aviation safety issues

Strategy 3: Revolutionary Autonomy

• Explore limits of knowledge and capabilities through grand challenges

• Enable future possibilities unconstrained by legacy systems and practices

www.nasa.gov 79


Strategies 2 and 3

Autonomy Advancement Paths Comparison

www.nasa.gov 80

Thrust 6 Strategy 2:

Evolutionary Autonomy (EA)

Thrust 6 Strategy 3:

Revolutionary Autonomy (RA)

• Opportunity-driven • Clean sheet design-driven

• Mission-enhancing; mission can be

performed without autonomy capabilities, but

not as well

• Mission-enabling; mission cannot be

performed without making use of autonomy

capabilities

• Perform existing functions in new ways using

autonomy capabilities

• Perform new functions using autonomy

capabilities

• Push technical advancements through

incremental establishment of value. Benefit:

leverages stakeholder investments

• Push technical advancements using risk-

seeking stretch challenges. Benefit: supports

technology breakthroughs

• Apply autonomy capabilities within existing

regulatory, infrastructure, and cultural

constraints

• Advance autonomy with few constraints

imposed by legacy systems, legacy

infrastructure, regulatory policies, or culture

2025 Vision (1 of 7)

All of the following anticipated capabilities are not envisioned to be in widespread use by 2025; rather we

anticipate that there will be varying degrees of advancement and implementation for each. The pace of

advancement for each capability will depend on many factors, the most significant of which will likely be

market demand and confidence in cost-benefit potential. However, we do believe that by 2025, each of

these capabilities can be advanced to demonstrations in a relevant environment, at a minimum.

1. Enabling New Airspace Uses, Users, and Vehicles

• Remotely operated UAS perform a variety of missions with routine access to airspace that is

dynamically controlled by air navigation service providers; information networks continually update UAS

operators of accessible airspace, as well as any changing conditions that may impact flights (e.g.

weather).

• Small UAS operate at low-altitude beyond visual line of sight to conduct missions such as precision

agriculture, infrastructure inspection, environmental monitoring, search and rescue, and first-responder

assistance. These operations will be organized through semi-autonomous UAS traffic management

services that will provide safety assurance and transparency to UAS operators, other stakeholders, and

the public.

• Teams of collaborating UAS perform coordinated missions in highly controlled, safety assured contexts.

The vehicles are controlled and managed by a single human operator or small team on the ground.

Missions have larger-scale geographic coverage for purposes such as environmental monitoring,

surveillance, and disaster relief.

www.nasa.gov 81


1. Enabling New Airspace Uses, Users, and Vehicles (continued)

• Experimental UAS systems can join to emulate a single-vehicle configuration to exploit range and

performance advantages and potentially enable new missions such as modular high altitude extremely

long endurance missions, and long-range search and rescue.

• For emergency/rescue operations, unmanned aircraft operate in support of manned aircraft and

ground-based first-responders by quickly providing critical information (e.g., locating people in need of

rescue, detecting hazards, etc.).

• UAS autonomously re-plan missions or safely end missions in response to changing conditions.

Replanning is based on pre-established criteria for conforming to operational flight rules or human

operator approval.

www.nasa.gov 82


2. Improving Safety Where Needed for Existing Missions and Assuring Safety for New Missions

• Missions with elevated risk factors make use of mission-specific, context-relevant decision aids, haptic

interfaces, sensors, and guidance systems to continuously evaluate flight risks and advise or assist

operators with mission replanning support and appropriate flight guidance. For example, emergency

medical evacuation flights operate using systems that assist operators in mission replanning based on

risks that may be unknown at takeoff, and provide active guidance during flight into and out of

unprepared sites. Similarly, single-pilot air taxi flights into remote areas operate safely by self-mitigating

encountered risks that would otherwise be mitigated by air traffic control or airport infrastructure.

• Experimental decision assistance systems in commercial aircraft evaluate flight risks and assist

operators to reduce complexity and operator workload during off-nominal flight situations and

conditions. Onboard vehicle-state diagnostic and predictive capabilities inform decision-making

functions of critical markers trending to unsafe states. Predictions regarding encountering unexpected

conditions are assessed in-flight based on information coming from onboard and off-board sources.

Systems assure that information can be trusted and actively prioritized based on context. Vehicle

system health information is reported to ground-based tracking and archiving systems to build historical

databases in support of initial and future system-wide safety assurance systems.

• Onboard systems provide advisories to human operators based on (a) observations from all available

sources, (b) learning based on prior similar experiences of the vehicle or other vehicles, and (c)

bounded predictions of what may occur if certain decision sequences are acted on in the presence of

input and environmental uncertainties. Systems evaluate option spaces within time-varying authority

constraints to suggest best course of action that meets both mission and safety objectives.

www.nasa.gov 83


2. Improving Safety Where Needed for Existing Missions and Assuring Safety for New Missions

(continued)

• Highly-assured context-relevant sensing, decision making, and execution systems are capable of

assuming control of vehicles in some circumstances to prevent accidents due to extreme and

immediate hazards that are not manageable within human limits. Resilient control architectures support

autonomous replanning and reconfiguration. Applications include autonomous landing after pilot

incapacitation; reconfiguration of available control surfaces after failures or departure from controlled

flight; and maneuvering to avoid terrain and other external hazards.

• Access to low-altitude airspace by UAS is dynamically controlled and continually updated by semi-

autonomous UAS traffic management services to ensure that UAS operate in accordance with all rules

and regulations, and risks related to rogue and intruder vehicles are safely mitigated.

• Onboard assured containment or conformance systems help to ensure that UAS fly in approved

operating areas and pose no threat to public safety, privacy, security, or property.

• Without requiring human intervention, some UAS can avoid collisions with other air vehicles, terrain,

and structures for missions requiring close proximity to these potential hazards.

• Without requiring human oversight, onboard systems monitor, assess, and predict vehicle needs for

maintenance or upgrades. In experimental systems, sensor networks embedded in vehicle structures

monitor critical internal loads and structural health/damage characteristics in real-time and during

ground checks for specific subsystems. High-fidelity, physics-based models ascertain structural

degradation and repair requirements.

www.nasa.gov 84


3. Improving Operational Efficiency to Reduce Environmental Impact, Air Traffic Congestion,

Passenger Journey Times, and Consumer Costs

• A data sharing infrastructure provides reliable high-bandwidth low-latency two-way communications

between aircraft, air navigation service providers, and ground-based organizations such as airline

operations centers and fixed base operators. The infrastructure enables vehicle-level and airspace

system-level health-monitoring, assessment, and prognostics through use of initial intelligent systems

that analyze real-time data streams and historical databases to develop in-time mitigation strategies.

The infrastructure enables vehicle-to-vehicle and vehicle-to-ground coordination to realize management

of traffic by trajectory (Trajectory-Based Operations), and facilitates the introduction of a diverse set of

new business/operating models that leverage network-centric principles, such as resource pooling, to

achieve new operating efficiencies.

• Operators and air navigation service providers collaborate using intelligent networked replanning

systems to continually optimize flight trajectories in response to evolving conditions. Continuous

optimization improves schedule conformance and operational efficiency for individual flights and airline

fleets, in addition to increasing overall system resilience in the presence of disruptions. Additional

optimization benefits are achieved by early adopters through use of experimental data analytics

systems that predict disturbances and anticipate operator responses, as well as through use of

automated air/ground trajectory negotiation.

• Advanced machine-based learning and data analytics systems balance air traffic demand and airspace

system capacity to improve traffic flow and reduce environmental impact.

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Passenger Journey Times, and Consumer Costs (continued)

• Machine-based learning algorithms provide advisories for integrated arrival, departure, and surface

operations, including reconfiguration of airport runways and gates at airports and within metroplexes,

enabling system-level throughput optimization and efficiency.

• Autonomous scheduling and routing systems enable new business models for personal air

transportation, in which the specifics of a trip, such as origin, destination, and departure time, are highly

tailorable by the customer.

• Ground-based air traffic scheduling algorithms generate formation flying opportunities to save fuel

during long-range high-altitude flight segments. Onboard systems utilize traffic surveillance data,

onboard sensors, and uplinked information to enable rendezvous and maintain spacing.

• Experimental autonomous tugs tow aircraft to and from runways, thereby demonstrating a new means

of reducing fuel use and improving the efficiency of surface operations.

• Experimental airborne systems automatically maintain optimal flight configurations, increase engine

efficiency, and reduce aircraft structural loads through use of predictive vehicle-specific models, internal

and external sensors, and advanced control schemes.

• Intelligent machine systems reliably perform many highly procedural tasks previously performed by

human operators and service providers. Allocation of ill-defined, unstructured tasks to intelligent

machine systems is limited to non-safety-critical functions, and is based on earned levels of trust. As

human operator and service provider roles evolve with the introduction of increasingly capable

intelligent machine systems, these systems are used to augment training and retention of new

knowledge and skill requirements.

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Passenger Journey Times, and Consumer Costs (continued)

• Vehicle design and manufacturing processes are optimized through use of advanced design tools and

model-based testing to reduce certification time and cost for new vehicles, including systems and

structures (e.g., advanced composite structures).

• Advances in natural language processing facilitate bi-directional communication between humans and

machines, enabling rapid automation support for a wide range of operator and service provider tasks,

decreasing communication errors, and reducing workload.

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