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National Aeronautics and Space Administration
ATIO-15
Special Session
Transformational Flight –
Autonomy
Aviation ...
2 of 19
National Aeronautics and Space Administration
Autonomy for Safety, Efficiency and
Mobility in Civil Aviation
B. Da...
3 of 19
Mobility: Anyone/thing, Anywhere, Anytime
4 of 19
NASA’s Missions and Humans
• Historic and current ATM and space exploration paradigms are
human-centric. Humans ar...
5 of 19
NASA’s Missions and Humans and Automation
• Historic and current ATM and space exploration paradigms are
human-cen...
6 of 19
NASA’s Missions and Autonomy
• As we move towards On-Demand Mobility (ODM) in aeronautics and
beyond LEO and L2 in...
7 of 19
Down The Rabbit Hole: Definitions
• Etymology: from the Greek,
– αὐτόματος (automatos) “self-moving,
self-acting, ...
8 of 19
• Automation/automated process: Replace manual process with
software/hardware that follow a programmed sequence. A...
9 of 19
Another Rabbit Hole: Scales of Autonomy
http://www.fas.org/irp/agency/dod/dsb/autonomy.pdf
10 of 19
June 2014 NRC Report
• Key Challenge
How can we assure that advanced
Increasingly Autonomous (IA) systems –
espec...
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Pilot self-separates
from all traffic and
Wx
ATC
separates
from IFR
Pilot separates
from Wx
Pilots see
and avoid
...
12 of 19
12PRE-DECISIONAL – FOR LARC CLC USE ONLY
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 …
DARPA TX ARES
O...
13 of 19
What does Autonomy enable and how?
• What
– Capabilities
• Reduced personnel and training
• Contingency/Emergency...
14 of 19
Safety  Efficiency  Mobility
CFIT
SPO
Go-Around
Adaptive
Autonomy
UAS in the
NAS
SWaP
State
Awareness
&
Health
...
15 of 19
Autonomy’s Impact on Avionics
Consequence
• Using adaptive systems
– systems that use real-time machine
learning ...
16 of 19
Autonomy’s Impact on Avionics
Consequence
• Using adaptive systems
– systems that use real-time machine
learning ...
17 of 19
Trust: Humans and (Ghost In the) Machines
• Trust: “Developing methods for establishing ‘certifiable trust in
aut...
Getting Ready for the Next Billion Dollar
Aerospace Industry—The Low Altitude Frontier
Thursday, 19 June 2014, 1400–1600
•...
19 of 19
danette.allen@nasa.gov, @DrDanetteAllen
Thank you
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Aviation 2014 Transformation Flight Special Session on Autonomy: Autonomy for Safety, Efficiency and Mobility in Civil Aviation

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

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Aviation 2014 Transformation Flight Special Session on Autonomy: Autonomy for Safety, Efficiency and Mobility in Civil Aviation

  1. 1. 1 of 19 National Aeronautics and Space Administration ATIO-15 Special Session Transformational Flight – Autonomy Aviation 2014 18 June 2014
  2. 2. 2 of 19 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
  3. 3. 3 of 19 Mobility: Anyone/thing, Anywhere, Anytime
  4. 4. 4 of 19 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.
  5. 5. 5 of 19 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.
  6. 6. 6 of 19 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)
  7. 7. 7 of 19 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/
  8. 8. 8 of 19 • 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
  9. 9. 9 of 19 Another Rabbit Hole: Scales of Autonomy http://www.fas.org/irp/agency/dod/dsb/autonomy.pdf
  10. 10. 10 of 19 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
  11. 11. 11 of 19 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
  12. 12. 12 of 19 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
  13. 13. 13 of 19 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
  14. 14. 14 of 19 Safety  Efficiency  Mobility CFIT SPO Go-Around Adaptive Autonomy UAS in the NAS SWaP State Awareness & Health Management Collaborative Trajectories PAV
  15. 15. 15 of 19 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
  16. 16. 16 of 19 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
  17. 17. 17 of 19 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. 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
  19. 19. 19 of 19 danette.allen@nasa.gov, @DrDanetteAllen Thank you

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