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

Early Design Requirements (101) My Strategy for winning the Cold War: We Win They Lose….

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

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

ASW MIW SUW IBGWN Cloud Enabled Common Operating Picture FORCEnet

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

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

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

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."

Sheridan Levels of Autonomy High 10 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 6 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 Low 1 The computer offers no assistance, human must take all decisions and actions.

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

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

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

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

Use Story for Early Design Requirements Development and Assessment for System Autonomy

Arctic Territorial Claims 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

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

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

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