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

My Strategy for winning the Cold War:

Current Politico-Military Requirements
Do This But Still Do This
• Cut Defense Budgets
– Do more with less
– Reduce Sustainment & Manpower
– Use more Systems Autonomy
– Move to the Cloud
• Maintain national objectives
– Increased situational awareness
– Meet National CYBER
Challenges & Demands
– Protect commercial shipping lanes and interests abroad

• 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

Cloud Enabled Common Operating Picture
100011100110011010101010101010011110010010101001010
FORCEnet
IBGWN
SUW
MIW
ASW

Navy Approach for Unmanned Systems
- CNO statement during June 2009 UxS CEB

“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 Operations
Naval Studies Board
Division on Engineering and Physical Sciences
National Research Council of the National Science Academies

• 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 computerexecuted tasks

“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."
• 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

High
10
6
The computer decides everything, acts autonomously, ignores the human
9 Informs the human only if it, the computer, decides to
8 Informs the human only if asked, or
7 executes automatically, then necessarily informs the human, and allows the human a restricted time to veto before automatic execution, or
5 executes that suggestion if the human approves, or
4 suggests one alternative
3 narrows the selection down to a few, or
2
The computer offers a complete set of decision/action alternatives, or
1
The computer offers no assistance, human must take all decisions and actions.
Low

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

Q2
Human Interaction
“level of autonomy” is a combination of human interaction and machine automation
Q1
Machine Automation
Q3 Q4

“level of autonomy” is a combination of human interaction and machine automation
F[SA] = F[MA] + F[HI]
Human Interaction
Levels of System Autonomy (SA) support or exceed Mission
Operation Needs
Levels of System Autonomy (SA)
DOES NOT support Mission
Operation Needs
SA
MCT
Machine Automation

Human Interaction
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
Tele-operation
ψ – technology angle
SA
Android
MCT set to 1
Machine Automation

Retreating Ice Cap Opens Territorial
Boundary Claims
Establishing Eminent Domain
Nationalizes Natural Resources

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

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

• 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

• 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