Early Design Requirements Development and Assessment for System Autonomy 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 (101) My Strategy for winning the Cold War: We Win They Lose…. Current Politico-Military Requirements Do This • Cut Defense Budgets – – – – But Still Do This • Maintain national objectives – Increased situational awareness Do more with less – Meet National CYBER Reduce Sustainment & Manpower Challenges & Demands Use more Systems Autonomy – Protect commercial shipping lanes and interests abroad Move to the Cloud 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 Cloud Enabled Common Operating Picture 100011100110011010101010101010011110010010101001010 MIW FORCEnet ASW IBGWN SUW 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 computerexecuted tasks Defining Levels of Autonomy “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 Sheridan Levels of Autonomy High Low 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 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 System Autonomy Human Interaction Q2 “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 MCT SA Levels of System Autonomy (SA) DOES NOT support Mission Operation Needs 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 ψ – technology angle Tele-operation MCT set to 1 SA Machine Automation Android 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. • • • Speed - 6 knot, endurance – 45 days, side scan sonar swath 12 meters Estimated transit 7 days Estimated ocean survey – 21 days SEADART Candidate surveillance, reconnaissance and data gathering (ISR) UUVs. Mature proven design in wide use • 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 • • • Speed - 10 knot, endurance – 40 days, side scan sonar swath 8 meters Estimated transit 4 days Estimated ocean survey – 22 days SEASWARM Candidate surveillance, reconnaissance and data gathering (ISR) UUVs. Under development • • 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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