1 Integrity Service Excellence Autonomy Challenges From an AFRL
Perspective 7 August 2014 Kris Kearns AFRL Portfolio Lead for
Autonomy Research [email protected] Human Effectiveness
Directorate (711HPW/RH) Air Force Research Laboratory
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Flow of Talk What I mean by Autonomy Autonomy Problem Space
Technical Challenges With brief intro to AFRL technologies AFRL
Strategy and Enduring Problem Areas
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3 AutomationAutonomy Automation and Autonomy The system
functions with no/little human operator involvement; however, the
system performance is limited to the specific actions it has been
designed to do. Typically these are well-defined tasks that have
predetermined responses (i.e., simple rule-based responses. Systems
which have a set of intelligence- based capabilities that allow it
to respond to situations that were not pre- programmed or
anticipated in the design (i.e., decision-based responses).
Autonomous systems have a degree of self-government and
self-directed behavior (with the humans proxy for decisions).
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4 Autonomous Systems and Technologies Cut Across Domains Cyber
systems handle massive, distributed, and data/information-
intensive tasks Aircraft systems operate in complex environment
needing to synchronize space and mission mgmt Weapons systems that
coordinate mission execution Space once launched systems must
operate on their own in a harsh environment
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5 Manpower efficiencies Rapid response 24/7 presence Harsh
environments New mission requirements .. Across Operational Domains
Decentralization, Uncertainly, ComplexityMilitary Power in the 21
st Century will be defined by our ability to adapt adaptation is
THE underlying foundation of autonomous technology Key AF
Challenges Addressed by Autonomy
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6 Todays Unmanned Systems Challenge Unmanned Air Vehicle
Leadership Admin & Overhead PilotsSensors Ops MaintMission
Coord Processing, Exploitation, Dissemination (PED) Full Motion
Video Signal Intelligence Maint Pilots Sensor Ops Maintenance DoD
Unmanned Vehicles are Unmanned in Name Only...
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Getting Beyond the One-to-One Paradigm One Operator for One
Ground Control Station One Ground Control Station to One Platform
One INT data stream to One Processing, Exploitation, Dissemination
Cell
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8 UAS Autonomy & Teaming UAS Autonomy & Teaming: Key
Goals Expand the available action space and decision space Operate
in contested and denied environments Increase coordination between
assets React faster than the opponents decision cycle Develop and
demonstrate the control and autonomy technologies required to
enable robust, adaptive, and coordinated combat operations by
heterogeneous, mixed teams of air assets Cooperative ISR challenge
is to provide ISR as an off-board service without the need to
directly control the UAS team Future is Tactical Battle Manager
(TBM) for multi-vehicle combat operations, supporting team mission
execution in contested and denied environments
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9 Advanced Interfaces for Multi-UAV Control Developing
interface technologies to optimize human performance (increased
decision-making, decreased stress, etc) Findings: Performance was
significantly improved for 8 of the 12 task types when the timeline
display was present. Operators also reported lower perceived
cognitive workload with the timeline tool.
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10 Balancing Operator Involvement w/ Automation Developing
Human-in-the-Loop Testbeds Objectively and subjectively measure
human performance Physiological (Eye tracking, ECG, voice analysis)
Subjective (Situational Awareness, Trust, Usability) Mission
Performance to ensure an optimized human-machine team MULTI-UAV
TESTBED Findings: Task performance was significantly better when
Levels of Automation were similar across tasks, compared to when
they differed. ANALYST TESTBED
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11 Human State Measurement & Assessment Developing
measurement techniques for stress, workload, attention. Correlating
human cognitive tasks to performance Long Term vision: Providing
the machine data about the humans state so the machine can aid
mission performance Human State Sensing foundational for humans and
machines to work as a team
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Autonomous System Certification Tomorrow, Decision-Making
Systems State-Space Explosion Unpredictable Environments
Human-Machine Communication Tomorrow, Decision-Making Systems
State-Space Explosion Unpredictable Environments Human-Machine
Communication Future, Learning Systems Emergent Behavior Complex,
unpredictable Environments Future, Learning Systems Emergent
Behavior Complex, unpredictable Environments complexity GAP TODAY:
Missing V&V Tools to enable Fielding of Autonomous Systems GAP
TODAY: Missing V&V Tools to enable Fielding of Autonomous
Systems
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13 Trust & Certification Trust & Certification: Key
Challenges Insufficient tools to V&V highly complex,
software-intensive systems Adaptive/learning systems and uncertain
environments yield near infinite state systems System composition
results in potentially hazardous emergent behavior Engaging a
national team of expertise across DoD, NASA, NSF, DoT, etc. to
develop new software certification methods, enabling greater
degrees of trust in highly complex, software intensive autonomous
systems Design for Certification asks how: to supplement test with
formal verification to automate test case generation / reduction
(Non-Statistical DoE for Software) Formal definition and
verification of rqmts & designs to reduce implementation errors
and cost in early stages
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14 UAS Airspace Integration UAS Airspace Integration:
Capability Progression Sensor, vehicle control algorithms, and
pilot interface development and flight test Common Airborne Sense
and Avoid system, scalable to Group 3-5 Terminal Area Operations
for safe, efficient ground and terminal operations Onboard sensors
such as radar, EO/IR, TCAS, and ADS-B will enable detection of both
cooperative and non-cooperative aircraft, providing protection in
all classes of airspace. The ABSAA system will provide autonomous
maneuvering or Pilot-In/On-The-Loop capability as operations
dictate. Key to success is exhibiting pilot-like behavior that
allows seamless integration into normal flight operations
Slide 15
Non-Technical Challenges (Culture) Drones Public perception
right or wrong complicates acceptance AF Pilot History A pilot has
always been in control of the aircraft General hurdle for All New
Technologies Single failure complicates acceptance of follow-on
technologies How to establish CONOPs/uses
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16 Ensure safe and effective systems in unanticipated &
dynamic environments AFRL Autonomy Vision & Goals Create
actively coordinated teams of multiple machines Ensure operations
in complex, contested environment AFRL Autonomy Vision Demonstrate
highly effective human-machine teaming Intelligent machines
seamlessly integrated with humans - maximizing mission performance
in complex and contested environments
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17 Ensure safe and effective systems in unanticipated &
dynamic environments AFRL Autonomy Human-Machine Teaming Create
actively coordinated teams of multiple machines Ensure operations
in complex, contested environment Demonstrate highly effective
human-machine teaming Intelligent machines seamlessly integrated
with humans - maximizing mission performance in complex and
contested environments
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18 Intelligent machines seamlessly integrated with humans -
maximizing mission performance in complex and contested
environments Ensure safe and effective systems in unanticipated
& dynamic environments AFRL Autonomy Human-Machine Teaming
Create actively coordinated teams of multiple machines Ensure
operations in complex, contested environment Demonstrate highly
effective human-machine teaming Enable & Calibrate trust
between human and machines Develop common understanding and shared
perception between humans and machines Create an environment for
flexible and effective decision making Enable & Calibrate trust
between human and machines Develop common understanding and shared
perception between humans and machines Create an environment for
flexible and effective decision making Transparency Communication
Training Sensing Interfaces
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21 AFRL Autonomy Coordinated Teams of Machines Ensure
operations in complex, contested environment Demonstrate highly
effective human-machine teaming Ensure safe and effective systems
in unanticipated & dynamic environments Create actively
coordinated teams of multiple machines Intelligent machines
seamlessly integrated with humans - maximizing mission performance
in complex and contested environments
Slide 22
22 AFRL Autonomy Coordinated Teams of Machines Ensure
operations in complex, contested environment Demonstrate highly
effective human-machine teaming Ensure safe and effective systems
in unanticipated & dynamic environments Create actively
coordinated teams of multiple machines Intelligent machines
seamlessly integrated with humans - maximizing mission performance
in complex and contested environments Mature machine intelligence
Develop and manage fractionated and composable systems Develop
reliable, secure, interoperable communication Mature machine
intelligence Develop and manage fractionated and composable systems
Develop reliable, secure, interoperable communication Communication
Perceive Reasoning Training Sense Act Plan
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23 AFRL Autonomy Complex & Contested Environments
Demonstrate highly effective human-machine teaming Ensure safe and
effective systems in unanticipated & dynamic environments
Create actively coordinated teams of multiple machines Ensure
operations in complex, contested environment Intelligent machines
seamlessly integrated with humans - maximizing mission performance
in complex and contested environments
Slide 24
24 AFRL Autonomy Complex & Contested Environments
Demonstrate highly effective human-machine teaming Ensure safe and
effective systems in unanticipated & dynamic environments
Create actively coordinated teams of multiple machines Intelligent
machines seamlessly integrated with humans - maximizing mission
performance in complex and contested environments Ensure operations
in complex, contested environment Develop technologies that assure
robust system and self- protection capabilities Develop
technologies that enable situational understanding of the
environment Develop technologies that assure robust system and
self- protection capabilities Develop technologies that enable
situational understanding of the environment Situational Awareness
Self Awareness Self Protection Perception Sense
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25 AFRL Autonomy Test & Evaluation, Verification &
Validation Create actively coordinated teams of multiple machines
Ensure operations in complex, contested environment Demonstrate
highly effective human-machine teaming Ensure safe and effective
systems in unanticipated & dynamic environments Intelligent
machines seamlessly integrated with humans - maximizing mission
performance in complex and contested environments
Slide 26
26 AFRL Autonomy Test & Evaluation, Verification &
Validation Create actively coordinated teams of multiple machines
Ensure operations in complex, contested environment Demonstrate
highly effective human-machine teaming Ensure safe and effective
systems in unanticipated & dynamic environments Intelligent
machines seamlessly integrated with humans - maximizing mission
performance in complex and contested environments Provide assurance
for machine intelligence and decision-making in complex, uncertain,
and dynamic environments Develop methods to ensure reliability of
human-machine communication and interaction Develop rigorous and
verifiable architecture systems for data centric autonomous systems
Develop methodology to V&V fractionated and composable systems
Provide assurance for machine intelligence and decision-making in
complex, uncertain, and dynamic environments Develop methods to
ensure reliability of human-machine communication and interaction
Develop rigorous and verifiable architecture systems for data
centric autonomous systems Develop methodology to V&V
fractionated and composable systems Standards Precedence Structured
Traceable Reusable Composable Evidence
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27 Questions
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28 AFRL Autonomy Team RH Mike Patzek, RHCI* Mark Draper, RHCI
Scott Galster, RHCP Jeff Graley, RHXM RI Jerry Dussault, RISB Rick
Metzger, RIS* RQ Bob Smith, RQCC** Corey Schumacher, RQCA* Jake
Hinchman, RQCC RV Scott Erwin, RVSV Paul Zetocha, RVSV* Khanh Pham,
RVSV RW Rob Murphey, RWW Will Curtis, RWWN* TJ Klausutis, RWW Ric
Wehling, RWWI RY Raj Malhotra, RYAR* OSR Tristan Nguyen, RTC
Autonomy Research conducted at many of the AFRL Technical
Directorates
Testing and Verifying Autonomous Systems Key Questions Will It
Make The Correct Decision When Encountering Expected, Unexpected Or
Unknown Situations? Types of correct decisions? For safety For
mission completion For rational behavior How Trustworthy Is The
Information, given its current situational awareness? How Do You
Prevent Undesired Emergent Behavior, as systems interact? Key
Challenges Insufficient tools to V&V highly complex,
software-intensive systems Adaptive/learning systems and uncertain
environments yield near infinite state systems System composition
results in potentially hazardous emergent behavior Ensuring
autonomous machines are safe and behave as specified