Systems Engineering Processes for M&S-based Development and Testing of Autonomous Capabilities

1. Systems Engineering Processesfor M&S-based Development and Testingof Autonomous Capabilities Andreas Tolk, Ph.D.Chief Scientist SimIS Inc.Adjunct Professor Old Dominion UniversityPortsmouth, Virginia, USA

2. Structure of the Presentation • Systems Engineering Processes • Systems Engineering • System of Systems Engineering • System Architectures • Autonomous Capabilities • Taxonomy of an Autonomous System • Levels of Autonomy • Modeling and Simulation • Taxonomy of an Intelligent Software Agent • Modeling as Theory Building • M&S-based Development and Testing

3. Topic One Systems Engineering Processes

4. Systems Engineering • Systems Engineering Processes ensure that • capability gaps are identified, • requirements are captured, • system functionality is derived and located to components, and the resulting system is defined regarding operations, performance, test, manufacturing, cost and schedule, training and support, and disposal. • The systems engineering processes bring team members from various system phases and stake holders for the whole system life cycle together to ensure that the resulting system meets the specifications.

5. Life Cycle Phases Operation Test Performance Manu-facturing Cost & Schedule Training &Support Disposal

6. System of Systems • Operational Independence of the Individual Systems • independent and useful in their own right • component are capable of independently performing useful operations • Managerial Independence of the Systems • operate independently • maintain a continuing operational existence • GeographicDistribution • Often large geographic dispersion • Emergent Behavior • behaviors that is not the behavior of any component system • EvolutionaryDevelopment • never fully formed or complete • systems evolve over time

7. Summary: SE/SoSE Processes • Processes needed for all • Phases of the life cycle • Team members • Stake holders • Common System Architecture captures • All facets and views needed • Functionality required for the system • Common model of components and interfaces • Context/Environment • Other systems and objects of interest System Architecture should be the common Knowledge Repository for all team members and stake holders of all phases.

8. Topic Two Autonomous Capabilities

9. Autonomy • Autonomy • Origin: Autos : SelfNomos : LawHaving a self-government, independent from others • The capability of a system to make decisions about its actions without the involvement of another system or an operator. This also entails entrusting the system to make these decisions. • The ability of integrated sensing, perceiving, analyzing, communicating, planning, decision-making, and acting/executing, to achieve its goals as assigned.(Autonomy Level for Unmanned Systems (ALFUS, NIST) • Automation • Using control systems and information technology to reduce the need for human intervention within well defined constraints

10. Taxonomy of an Autonomous System • Locomotion components • Moving the system in its environment (different degrees of freedom) • Actuator components • Moving parts of the autonomous system (robot arms, sensors, antennas, etc.) • Manipulation components • Interacting with environment (grab, push, turn, etc.) • Sensor components • Observing the environment (all kind of sensors) • Signal processing components • Converts sensor signals into computable information • Converts computed information into actuator signals • Controlcomponents • “Brain” of the autonomous systems, makes the decisions • Communication components • Exchange information with others (robots, as well as humans) • Power supply components • Energy source (usually battery or solar panel)

11. Levels of Autonomy • Teleoperation • Remote control systems • Supervisory • Control with the human, certain specific movements left to the system • Task-level Autonomy • Operator specifies the task, system executes it • Full Autonomy • No human interaction, system creates and completes tasks • Autonomy Levels for Unmanned Systems (ALFUS); NIST Mission Complexity Human Independence Environmental Complexity

12. Topic Three Modeling & Simulation

13. Intelligent SW Agent Taxonomy

14. Intelligent SW Agent Characteristics

15. How do Intelligent SW Agents “understand?” Observing System Observed System Properties Concepts Processes Constraints 1 2 4 3 Sensor Perception Mapping Meta-Models

16. Topic Four M&S-based Development and Testing

17. Autonomous Systems vs. Intelligent Agents Intelligent Agents Autonomous Systems Real sensors used for perception Many actions are reactions on sensor input Bumper switches Optical sensor Complex sensors require complex mapping Camera Communication possible Locomotion, actuators, and manipulators used to execute action • Sensor-based perception • Propertied concept based object recognition • Model-based situation recognition using adaptable memory • Socially competent • Utility-driven decision making • Action layer to execute decision

18. Recommendation • Intelligent SW Agents have much in common with Autonomous Capabilities • Mapping of Taxonomies possible • Same form of machine understanding, learning, planning, etc. • Properties of M&S-based Solutions • Full control of the environment (environmental complexity) • Full control of the tasks (mission complexity) • Intelligent SW agents represent all algorithms needed • Sense making • Communication • Planning • Decision making • Learning Agent-based Solutions optimally represent Autonomous Capabilities inVirtual Environments configured by Systems Architecture Artifacts

19. Example – US Navy Riverscout

20. Questions / Point of Contact Andreas Tolk, PhD Chief Scientist SimIS 200 High Street #305 Portsmouth, VA 23704 United States andreas.tolk@simisinc.com