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Integrity Service Excellence

AFRL Autonomy

11 July 2013

Dr. Jim Overholt AFRL Senior Scientist for Autonomy

Human Effectiveness Directorate (711HPW/RH) Air Force Research Laboratory

Kris Kearns AFRL Autonomy Portfolio Lead

Human Effectiveness Directorate (711HPW/RH) Air Force Research Laboratory

Distribution A. Approved for public release: distribution unlimited. (88ABW-2013-3169, 10 July 2013)

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Topics

AFRL Autonomy

Autonomy Challenges in Air Domain

DoD Autonomy Priority Steering Council (PSC) & Community of Interest (COI)

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Not really!

in Name Only. . .

Unmanned Air Vehicle

Unmanned Air Vehicle Leadership

Admin & Overhead

Pilots Sensors Ops

Maint Mission Coord

Processing, Exploitation, Dissemination (PED)

Full Motion Video

Signal Intelligence

Maint

Pilots

Sensor Ops

Maintenance

The Automation-Autonomy Continuum

The system functions with no/little human operator involvement, well-defined tasks that have predetermined responses

Automation Systems have intelligence-based capabilities, respond to situations not pre-programmed or anticipated in design

Autonomy

Autonomy is a capability that enables a particular action of a system to be automatic or, within programmed boundaries, “self-governing.”

(The Role of Autonomy in DoD Systems, Defense Science Board, July 2012)

― Manpower efficiencies ― Rapid response and 24/7 presence ― Harsh environments ― New mission requirements ― Capabilities beyond human limits …….. Across Operational Domains

Decentralization, Uncertainly, Complexity…Military Power in the 21st Century will be defined by our ability to adapt – adaptation is THE underlying foundation of

autonomous technology

Key DoD Challenges Addressed by Autonomy

Some Shared Autonomy S&T Challenges for DoD & Industry Partners

Human/Autonomous System Interaction and Collaboration

Test, Evaluation, Validation, and Verification

Scalable teaming of Autonomous Systems

Machine Perception, Reasoning and Intelligence

Future R&D must provide secure communication between agents and their operators, expand shared perception and problem solving across multiple agents, and advance guidance/control

Future R&D must further integrate artificial intelligence & human cognitive models, advance human-agent feedback loops, optimize trust/transparency, and advance sensor/data decision models

Future R&D must advance data-driven analytics, contingency-based control strategies, decision making algorithms to enable operations, and adaptive guidance & control

Must expand its TEV&V capabilities in live and simulated environments across all operational domains. Test beds must incorporate scenario-based testing.

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Topics

AFRL Autonomy

Autonomy Challenges in Air Domain

DoD Autonomy Priority Steering Council (PSC) & Community of Interest (COI)

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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

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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

10 Ensure safe and effective systems in unanticipated & dynamic environments

AFRL Autonomy Vision & Goals

Intelligent machines seamlessly integrated with humans - maximizing mission performance in

complex and contested environments

Create actively coordinated teams of multiple machines

Ensure operations in complex, contested environment

Demonstrate highly effective human-machine teaming

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Ensure safe and effective systems in unanticipated & dynamic environments

AFRL Autonomy Goal Human-Machine Teaming

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

Demonstrate highly effective human-machine teaming

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Ensure systems are safe and effective in a wide variety of unanticipated and rapidly evolving environments

AFRL Autonomy Human-Machine Teaming

Intelligent machines seamlessly integrated with humans - maximizing mission performance in

complex and contested environments

Create actively coordinated teams of multiple machines

Ensure operations in complex, contested environment

Demonstrate highly effective human-machine teaming Transparency

Communication

Training Sensing

• 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

Interfaces

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SENSE

ASSESS AUGMENT

attached to skin

detached f rom skin

5mm

Human-Machine Teaming Augmentation Framework

•Extraction of objective human measures to inform empirical studies

•Implement a controlled feedback cycle

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Human State Measurement

• Developing measurement techniques for stress, workload, attention.

• Correlating human cognitive tasks to performance

• Long Term vision: Providing the machine data about the human’s 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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Advanced Interfaces for Multi-UAV Control

Developing interface technologies to optimize human performance (increased decision-making, decreased stress, etc)

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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 ANALYST TESTBED

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Ensure systems are safe and effective in a wide variety of unanticipated and rapidly evolving environments

AFRL Autonomy Goals and Major Objectives

Intelligent machines seamlessly integrated with humans - maximizing mission performance in

complex and contested environments

Ensure operations in complex, contested environment

Demonstrate highly effective human-machine teaming

• Mature machine Intelligence

• Develop and manage fractionated and composable systems

• Develop reliable, secure, interoperable communication

Create actively coordinated teams of multiple machines

Communication

Perceive Reasoning

Training

Sense

Act

Plan

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Machine Self-Awareness: Adaptation to Degradations in Systems Health

Autonomous Systems need to be responsive to systems health • Determination of failure, or impending failure • Reconfiguration of control to allow for safe recovery, or • Adaptation to enable continued mission operations

Hierarchical health diagnosis architecture with feedback and reasoning for

disambiguation

Adaptive inner-loop (stability) and outer-loop (trajectory) control to recover from failures

System Health Reasoner

Subsystem State

Assessment

Subsystem State

Assessment

Subsystem State

Assessment

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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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Ensure systems are safe and effective in a wide variety of unanticipated and rapidly evolving environments

AFRL Autonomy Goals and Major Objectives

Intelligent machines seamlessly integrated with humans - maximizing mission performance in

complex and contested environments

Create actively coordinated teams of multiple machines

Demonstrate highly effective human-machine teaming

• Develop technologies that assure robust system and self-protection capabilities

• Develop technologies that enable situational understanding of the contested environment

Ensure operations in complex, contested environment

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Key enabling technology for integrating unmanned air vehicles into manned airspace – provides automated

“see & avoid” required of piloted aircraft

Deconfliction/Conflict Avoidance

• Demo’ed single-intruder autonomous detect/avoid using EO/TCAS (Dec 06) • Demo’ed single-, two-intruder closed-loop detect/avoid using EO/TCAS/ADS-

B/surrogate radar (Aug & Sep 09) • Final demo of single-, two-intruder closed loop detect/avoid with improved EO,

SAA radar, ADS-B, and TCAS

Sense & Avoid (SAA)

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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

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Ensure systems are safe and effective in a wide variety of unanticipated and rapidly evolving environments

AFRL Autonomy Goals and Major Objectives

• 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

Intelligent machines seamlessly integrated with humans - maximizing mission performance in

complex and contested environments

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

Will it make the correct decision when encountering expected, unexpected or unknown situations?

How trustworthy is the information, given its current situational awareness?

How to prevent undesired emergent behavior, as systems interact?

• 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

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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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Topics

AFRL Autonomy

Autonomy Challenges in Air Domain

DoD Autonomy Priority Steering Council (PSC) & Community of Interest (COI)

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Autonomy (Theory or Not)

Human Factors Engineering Machine Algorithmic Development

Partially Observable Markov Decision

Process

Perf

orm

ance

Arousal

Fitts’ Law

?

Strong Weak Low High

Transformed value function for all observations

Yerkes Dodson Law

Theory Linking The Two Sides is Lacking, Mostly Empirical

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Big Challenges/Issues

• Human-Machine Teaming – Common understanding & perception – Trust in autonomy issues – Shared decision making

• Scalable Teaming – Fractionated and composable systems – Reliable, secure, interoperable communication

• Contested Environments – Situational understanding of the contested environment

– Robust sense and avoid in all conditions

• T&E and V&V – Modeling and simulation – V&V of highly complex, software-intensive systems – Design for certification

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Questions

Dr. Jim Overholt AFRL Senior Scientist for Autonomy Human Effectiveness Directorate (711HPW/RH) Air Force Research Laboratory James.Overholt@wpafb.af.mil Office: 937-938-3968 Mobile: 937-829-1179

Kris Kearns AFRL Autonomy Portfolio Lead Human Effectiveness Directorate (711HPW/RH) Air Force Research Laboratory Kristen.Kearns@wpafb.af.mil Office: 937-656-9758 Mobile: 937-430-4897