NASA LaRC's Autonomy Incubator conducted a special session on Autonomy in Civil Aviation at Aviation 2014 in Atlanta, GA.

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National Aeronautics and Space Administration
ATIO-15
Special Session
Transformational Flight –
Autonomy
Aviation 2014
18 June 2014

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National Aeronautics and Space Administration
Autonomy for Safety, Efficiency and
Mobility in Civil Aviation
B. Danette Allen, PhD
Chief Technologist for Autonomy
NASA Langley Research Center (LaRC)
Aviation 2014
18 June 2014

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Mobility: Anyone/thing, Anywhere, Anytime

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NASA’s Missions and Humans
• Historic and current ATM and space exploration paradigms are
human-centric. Humans are aided by automation to make intelligent
decisions and intervene as needed, especially in off-nominal situations.
Five of the seven Apollo missions that attempted to land on the
Moon required real-time communications with controllers to succeed.

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NASA’s Missions and Humans and Automation
• Historic and current ATM and space exploration paradigms are
human-centric. Humans are aided by automation to make intelligent
decisions and intervene as needed, especially in off-nominal situations.
Things have changed but...
Humans are still hovering around monitors waiting to intervene.

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NASA’s Missions and Autonomy
• As we move towards On-Demand Mobility (ODM) in aeronautics and
beyond LEO and L2 in space exploration, human intelligence applied to
supervision, control, and intervention of operations will no longer be
viable due to system/mission complexity, short reaction/decision time,
comm delays, distance, hostile environments, and close proximity.
• This requires that we design, build, and test systems capable of
responding to expected and unexpected situations with machine
intelligence similar to that of humans. This means
– trusted and certified-safe systems capable of
– sensing and perception
– situation assessment/awareness
– decision-making
– taking action
– and knowledge acquisition (learning)

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Down The Rabbit Hole: Definitions
• Etymology: from the Greek,
– αὐτόματος (automatos) “self-moving,
self-acting, spontaneous”),
– αὐτονομία (autonomia), from αὐτός
(autos, “self”)+νόμος (nomos, “law”).
• au·ton·o·mus
– Definition: Acting on one's own or independently; acting without
being governed by parental or guardian rules.
– Synonyms: Self-governing, intelligent, sentient, self-aware,
thinking, feeling, self-directed
– Machine-based decision-making
• au·to·ma·tik or au·to·ma·shun
– Definition: Done out of habit or without conscious thought
– Synonyms: perfunctory, thoughtless, instinctive
– Machine-based execution
Source: http://en.wiktionary.org/wiki/

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• Automation/automated process: Replace manual process with
software/hardware that follow a programmed sequence. Automation
is a relegation of task(s).
• Autonomy: Allows participants to operate on their own, based on
internal goals, with little or no external direction. Participants can be
human or machines. Autonomy implies self-governance and self-
direction. Autonomy is a delegation of responsibility to the system
to meet goals.
• Autonomicity1: Builds on autonomy technology to impart
self-awareness to system/mission, which includes configuration,
optimization, healing, protection. These are enabled by self-
awareness, self-situation, self-monitoring, and self-adjustment
1Truszkowski, W., Hallock, L., Rouff, C., Rash, J., Hinchey, M.G., Sterritt, R. (2009) Autonomous
and Autonomic Systems: With Applications to NASA Intelligent Spacecraft Operations and
Exploration Systems
Relegation  Delegation  Self-Awareness

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Another Rabbit Hole: Scales of Autonomy
http://www.fas.org/irp/agency/dod/dsb/autonomy.pdf

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June 2014 NRC Report
• Key Challenge
How can we assure that advanced
Increasingly Autonomous (IA) systems –
especially those that rely on adaptive /
nondeterministic software – will enhance
rather than diminish the safety and reliability
of the NAS?
• National Research Agenda
– Behavior of adaptive / nondeterministic
systems
– Operations without continuous human
oversight
– Modeling and Simulation
– Verification, Validation and Certification
– Non-traditional Methodologies and
Technologies
– Roles of Personnel and Systems
– Safety and Efficiency
– Stakeholder Trusthttp://sites.nationalacademies.org/DEPS/ASEB/DEPS_046747

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Pilot self-separates
from all traffic and
Wx
ATC
separates
from IFR
Pilot separates
from Wx
Pilots see
and avoid
AFR
AFR
IFR
IFR
VFR
VFR
ATC
AFR
Automation/DSTs
ATD-1 / FIM / GIM
TASAR
SEVSSURFACE CD&R
Separation
Management
ITP
SEVS
Emergency
Landing
Planner
AFR
AAC

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12PRE-DECISIONAL – FOR LARC CLC USE ONLY
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 …
DARPA TX ARES
ONR AACUS TALOS
DARPA M3 Cheetah Wildcat ?
NRLSAFFiR
X-47B UCAS
Shadwell Test Functional Crew Member
Vehicle testing
UCLASS – 24/7 ISR w/ Strike CapabilityUCLASS
Autonomous aerial refueling
Industry solicitation
Driverless Cars Commercialized
NASA Robonaut
Prototypes
12
STS-133
Carrier-based launch
Functional Crew Member
DARPA CODE
DARPA ALIAS
The Autonomy Frontier
Concepts

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What does Autonomy enable and how?
• What
– Capabilities
• Reduced personnel and training
• Contingency/Emergency Management
• Holistic/System Health/Safety Management
– Missions
• Point-to-point transportation of cargo and people
• Agriculture
• Disaster response
• Long endurance ISR
– New paradigms
• Personal Air Vehicles
• How
– Systems that sense, perceive, adapt and learn
– Systems that self- protect, heal, configure, optimize
– Intelligent Flight Systems

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Safety  Efficiency  Mobility
CFIT
SPO
Go-Around
Adaptive
Autonomy
UAS in the
NAS
SWaP
State
Awareness
&
Health
Management
Collaborative
Trajectories
PAV

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Autonomy’s Impact on Avionics
Consequence
• Using adaptive systems
– systems that use real-time machine
learning and statistical methods to
mimic intelligence
• Needing improved system safety
methods to identify & mitigate
hazards
– especially related to human roles/
responsibilities
• Needing improved methods for
verification and validation that
enable us to trust autonomy in all
circumstances
– increased emphasis on validation
 did we get the requirements right?
Direct Effect
• Safety becomes increasingly
dependent on software/automation
• Role of the pilot becomes embedded
more than ever in the avionics
• Complete and correct requirements
become more important
– especially for contingency management
• Data integrity and availability
become more important
• Functionality moves further from
federated systems to complex,
integrated, network-centric system-
of-systems
– potentially more obscure error sources

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Autonomy’s Impact on Avionics
Consequence
• Using adaptive systems
– systems that use real-time machine
learning and statistical methods to
mimic intelligence
• Needing improved system safety
methods to identify & mitigate
hazards
– especially related to human roles/
responsibilities
• Needing improved methods for
verification and validation that
enable us to trust autonomy in
all circumstances
– increased emphasis on validation
 did we get the requirements right?
Direct Effect
• Safety becomes increasingly
dependent on software/automation
• Role of the pilot becomes embedded
more than ever in the avionics
• Complete and correct requirements
become more important
– especially for contingency management
• Data integrity and availability become
more important
• Functionality moves further from
federated systems to complex,
integrated, network-centric system-of-
systems
– potentially more obscure error sources

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Trust: Humans and (Ghost In the) Machines
• Trust: “Developing methods for establishing ‘certifiable trust in
autonomous systems’ is the single greatest technical barrier that
must be overcome to obtain the capability advantages that are
achievable by increasing use of autonomous systems.”
U.S. Air Force “Technology Horizons” 2010-2030, http://www.au.af.mil/au/awc/awcgate/af/tech_horizons_vol-1_may2010.pdf
• Trust is objective and subjective, technical and interpersonal
• Trust accommodates uncertainty – is probabilistic
• Trust is gained over time
• Interpersonal Trust is acquired
– Information
– Integrity
– Intelligence
– Interaction
– Intent
– Intuition
NASA Autonomy Validation Workshop, August 2012
Sponsored by NASA Office of Chief Technologist

18. Getting Ready for the Next Billion Dollar
Aerospace Industry—The Low Altitude Frontier
Thursday, 19 June 2014, 1400–1600
• This panel will discuss emerging opportunities in low-altitude airspace in various
parts of the world, including vehicles and airspace operations systems that are
needed to enable these operations safely. The low-altitude airspace operations
include, but are not limited to, unmanned aerial systems (UAS) and personal air
vehicles. The emerging businesses will include applications related to agriculture,
entertainment, search and rescue, cargo delivery, etc.
– Parimal Kopardekar, Manager, NextGen Concepts and Technology Development Project,
NASA Ames Research Center (Moderator)
– B. Danette Allen, Chief Technologist for Autonomy,
NASA Langley Research Center (Moderator)
– Jesse Kallman, Global Business Development & Regulatory Affairs, Airware
– Andrew R. Lacher, UAS Integration Research Strategist, The MITRE Corporation
– Rose Mooney, Executive Director, Mid-Atlantic Aviation Partnership
– Mark Moore, Senior Researcher, NASA Langley Research Center
– Alex Stoll, Aeronautical Engineer, Joby Aviation
• Livestreamed at http://www.new.livestream.com/AIAAvideo/Aviation2014
• #LowAltOps

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danette.allen@nasa.gov, @DrDanetteAllen
Thank you