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Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student 3-4 April 2014 Chantilly, VA Early Design Requirements Development and Assessment for System Autonomy

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Page 1: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Systems Engineering Conference Washington DC

Jerrel Stracener, SMU PhDCAPT Daniel P. Burns USNR, SMU PhD Student

Rusty Husar, SPAWAR, SMU PhD Student

3-4 April 2014 Chantilly, VA

Early Design Requirements Development and Assessment for

System Autonomy

Page 2: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Early Design Requirements (101)

My Strategy for winning the Cold War:

We Win

They Lose….

Page 3: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Current Politico-Military Requirements

Do This

• Cut Defense Budgets

– Do more with less– Reduce Sustainment & Manpower– Use more Systems Autonomy – Move to the Cloud

But Still Do This

• Maintain national objectives– Increased situational awareness– Meet National CYBER

Challenges & Demands– Protect commercial shipping

lanes and interests abroad

Page 4: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Who is Moving to the Cloud?

• Intelligence Community– IC Information Technology Enterprise– IC Cloud Hosting Environment

• Department of Defense– Joint Information Environment

– DoD Core Data Centers & DoD Cloud Hosting Environment

• Department of Navy– OPNAV – Task Force Cloud

– N2/N6 Navy TENCAP R&D functional lead

– ONI – Maritime ISR Enterprise– NCDOC – Naval Cyber Cloud

Navy is “All-In”Working Across Interagency Partners to Execute the Movement to

the Cloud

Page 5: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

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Cloud Enabled Common Operating Picture

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FORCEnet

Page 6: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Navy Approach for Unmanned Systems

A Maritime and Littoral force that integrates manned and Unmanned Systems (US) to increase capability across the full spectrum of Naval missions while remaining fiscally achievable.

- CNO statement during June 2009 UxS CEB

Page 7: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Mission Autonomy

“Recommendation 4: The Assistant Secretary of the Navy for Research, Development, and Acquisition (ASN(RD&A)) should mandate that level of mission autonomy be included as a required up-front design trade-off in all unmanned vehicle system development contracts.”

Committee on Autonomous Vehicles in Support of Naval OperationsNaval Studies Board

Division on Engineering and Physical SciencesNational Research Council of the National Science Academies

Page 8: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Autonomy vs. Automation

• Automation, autonomy, full autonomy – these terms are not synonymous

• Autonomy is a critical, yet potentially controversial attribute of unmanned systems

• From the US NAVY CNO – what is frequently referred to as a “level of autonomy” is

a combination of human interaction and machine automation– Not fully understanding autonomy has hindered development

of unmanned systems by the Navy– The degree of machine automation is not as easily categorized

• range of increasingly complex, computer-generated and computer-executed tasks

Page 9: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Defining Levels of Autonomy

• Defining Levels of Autonomy (LOA) in a simple, useable form has proven a difficult task• No single scale has been found acceptable• Autonomy – Automation: Often interchanged

• Intuitively, LOA could be characterized by position on a linear axis with manual operation at one end and full automation at the other• Intermediate levels of one scale often seem unrelated to those of

another• Therefore, we propose that our discussion of autonomy be

broken down into descriptions of human interaction and system automation

“Review the strategy for future development of autonomy in unmanned systems, including "sense and avoid" technology. Project the likely timeframe for development of full autonomy."

Page 10: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Sheridan Levels of AutonomyHigh

10 The computer decides everything, acts autonomously, ignores the human

9 Informs the human only if it, the computer, decides to8 Informs the human only if asked, or

7 executes automatically, then necessarily informs the human, and

6 allows the human a restricted time to veto before automatic execution, or

5 executes that suggestion if the human approves, or4 suggests one alternative3 narrows the selection down to a few, or

2 The computer offers a complete set of decision/action alternatives, or

Low1 The computer offers no assistance, human must take all

decisions and actions.

Page 11: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

AGILE and Rapid IT Development Initiatives

• Current AGILE and RAPID Information Technology (IT) programs drive the acceleration in the development of unmanned and autonomous systems and stress conventional development frameworks

Page 12: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Human Interaction

Machine Automation

Q1Q2

Q4Q3

“level of autonomy” is a combination of human interaction

and machine automation

System Autonomy

Page 13: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Human Interaction

Machine Automation

Levels of System Autonomy (SA) support or exceed Mission

Operation Needs

Levels of System Autonomy (SA) DOES NOT support Mission

Operation Needs

MCT

SA

F[SA] = F[MA] + F[HI]

“level of autonomy” is a combination of human interaction

and machine automation

Page 14: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Human Interaction

Machine Automation

Tele-operation

Android

MCT set to 1

ψ – technology angle

SA

System Autonomy treated as a vector• Scalar component - SA= √(MA^2+HI^2)• SA represents system capability• Angular component - Ψ= tan-1 [MA/HI]• Ψ represents technology base

Page 15: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Use Story for Early Design Requirements Development and Assessment for System

Autonomy

Page 16: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Arctic Territorial ClaimsRetreating Ice Cap Opens Territorial Boundary Claims

Establishing Eminent Domain Nationalizes Natural Resources

Page 17: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Complex System of Underwater Autonomous Systems Illustrative Concept #1

SEABOX Candidate Large Displacement UUV as transit and deployment platform deploys quantity 8 SEADART ocean survey UUVs. Under development.

SEADART Candidate surveillance, reconnaissance and data gathering (ISR) UUVs. Mature proven design in wide use

• Speed - 6 knot, endurance – 45 days, side scan sonar swath 12 meters

• Estimated transit 7 days• Estimated ocean survey – 21 days

• Speed - 5 knot, endurance – 5 days, side scan sonar swath 4 meters

Page 18: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Complex System of Underwater Autonomous Systems Illustrative Concept #1

SEAHORSE Candidate Large Displacement UUV as transit and deployment platform deploys quantity 48 SEASWARM ocean survey UUVs. Mature proven design in wide use

SEASWARM Candidate surveillance, reconnaissance and data gathering (ISR) UUVs. Under development

• Speed - 10 knot, endurance – 40 days, side scan sonar swath 8 meters

• Estimated transit 4 days• Estimated ocean survey – 22 days

• Speed - 3 knot, endurance – 3/4 days, side scan sonar swath 4 meters

• Develops an underwater collaborative network to perform ocean survey

Page 19: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Mission Timeline

• Develop time line for each candidate– Mission phases are very similar to ocean surveys done be

UUVs• Outline SA assessments used in very early AoA, CONOPS

and design concept phases

Page 20: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

Summary

• Autonomous systems are a complex integration of human intelligence supervising machine automation to adapt to unforeseen events encountered during operations

• Missions are becoming more complex and spiraling the need for ever-increasing autonomous systems

• An algorithmic relationship between the two major system components, human supervisor and unmanned machines, provides a tradeoff study capability to define requirements and assess complex architectures during early development phases

• DoD’s significant use of Complex Autonomous systems to provide– Situational awareness data– Battegroup coordination– Mission execution

• Current economic environments creates greater dependencies on complex adaptive systems to perform ISR and execute missions

Page 21: Systems Engineering Conference Washington DC Jerrel Stracener, SMU PhD CAPT Daniel P. Burns USNR, SMU PhD Student Rusty Husar, SPAWAR, SMU PhD Student

?? QUESTIONS ??