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How to use Modeling and Simulation (M&S) in support of roboticsTRANSCRIPT
A. Tolk, SimIS © 2014
This presentation was given in the plenary session of the first “Modeling and
Simulation for Autonomous Systems (MESAS)” workshop that was conducted in
Rome, Italy, May 5-6, 2014, organized by the M&S Center of Excellence
(MSCOE) of NATO.
The presentation was prepared and given by Dr. Andreas Tolk, Chief Scientist
at SimIS Inc., USA.
We like to thank our sponsor, Qbit Technologies, for their support to participate
in the inaugural MESAS event.
Andreas Tolk, Ph.D.Chief Scientist SimIS Inc.
Adjunct Professor Old Dominion University
Portsmouth, Virginia, USA
Systems Engineering Processes
for M&S-based Development and Testing
of Autonomous Capabilities
A. Tolk, SimIS © 2014
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
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A. Tolk, SimIS © 2014
SYSTEMS ENGINEERING PROCESSES
Topic One
A. Tolk, SimIS © 2014
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.
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Life Cycle Phases
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Operation
Test Performance
Manu-
facturingCost &
Schedule
DisposalTraining &
Support
A. Tolk, SimIS © 2014
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
Geographic Distribution
– Often large geographic dispersion
Emergent Behavior
– behaviors that is not the behavior of any component system
Evolutionary Development
– never fully formed or complete
– systems evolve over time
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A. Tolk, SimIS © 2014
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
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System Architecture should be the common Knowledge Repository
for all team members and stake holders of all phases.
A. Tolk, SimIS © 2014
AUTONOMOUS CAPABILITIES
Topic Two
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Autonomy
Autonomy
– Origin: Autos : Self Nomos : Law
Having 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
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A. Tolk, SimIS © 2014
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
Control components
– “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)
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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
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Environmental
Complexity
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MODELING & SIMULATION
Topic Three
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Intelligent SW Agent Taxonomy
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Intelligent SW Agent Characteristics
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How do Intelligent SW Agents “understand?”
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Observed
System
Perception Meta-ModelsMapping
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Observing
System
Properties
Concepts
Processes
Constraints
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Sensor
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M&S-BASED DEVELOPMENT
AND TESTING
Topic Four
A. Tolk, SimIS © 2014
Autonomous Systems vs. Intelligent Agents
Intelligent Agents
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
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
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A. Tolk, SimIS © 2014
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
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Agent-based Solutions optimally represent Autonomous Capabilities in
Virtual Environments configured by Systems Architecture Artifacts
A. Tolk, SimIS © 2014
Example – US Navy Riverscout
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A. Tolk, SimIS © 2014
Questions / Point of Contact
Andreas Tolk, PhD
Chief Scientist SimIS
200 High Street #305
Portsmouth, VA 23704
United States