NASA RASPBERRY SI

https://nasa-raspberry-si.github.io/raspberry-si/

Transcript

1. RASPBERRY SI
   David Garlan
   CMU
   Co-I
   Bradley Schmerl
   CMU
   Co-I
   Pooyan Jamshidi
   USC
   PI
   Javier Camara
   York
   Collaborator
   Ellen Czaplinski
   Arkansas
   Consultant
   Katherine Dzurilla
   Arkansas
   Consultant
   Jianhai Su
   USC
   Graduate
   Student
   Matt DeMinico
   NASA
   Co-I
   AISR: Autonomous Robotics Research for Ocean Worlds (ARROW)
   Resource Adaptive Software Purpose-Built for Extraordinary
   Robotic Research Yields - Science Instruments
   Abir Hossen
   USC
   Graduate
   Student
   

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2. K. MICHAEL DALAL
   Team Lead
   USSAMA NAAL
   Software Engineer
   LANSSIE MA
   Software Engineer
   Autonomy
   • Quantitative Planning
   • AI-based Mission Discovery
   • Transfer & Online Learning
   • Model Compression
   JIANHAI SU
   USC, Graduate Student
   BRADLEY SCHMERL
   CMU, Co-I
   DAVID GARLAN
   CMU, Co-I
   JAVIER CAMARA
   York, Collaborator
   MATT DeMINICO
   NASA, Co-I
   HARI D NAYAR
   Team Lead
   ANNA E BOETTCHER
   Robotics System Engineer
   ASHISH GOEL
   Research Technologist ANJAN CHAKRABARTY
   Software Engineer
   CHETAN KULKARNI
   Prognostics Researcher
   THOMAS STUCKY
   Software Engineer
   TERENCE WELSH
   Software Engineer
   CHRISTOPHER LIM
   Robotics Software Engineer
   JACEK SAWONIEWICZ
   Robotics System Engineer
   ABIR HOSSEN
   USC, Graduate Student
   ELLEN CZAPLINSKI
   Arkansas, Consultant
   KATHERINE DZURILLA
   Arkansas, Consultant
   POOYAN JAMSHIDI
   USC, PI
   RASPBERRY SI
   Physical Testbed Virtual Testbed
   AISR: Autonomous Robotics Research
   for Ocean Worlds (ARROW)
   CAROLYN R. MERCER
   Program Manager
   Develop
   Develop and
   maintain
   Evaluate
   Evaluate
   Develop and
   maintain
   

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3. Science Discovery in Remote Planets
   The Phoenix Mars Lander
   Image Credit: NASA/JPL
   

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4. Current Practice of Science Discovery in Remote Planets
   Problem: Slow science discovery due to lack of full autonomy
   4
   (v) Only deal with:
   Known Knowns
   ~2.5 hours
   ~2.5 hours
   (iv) Does not scale
   (i) Delay in science discovery
   (iii) High risks
   (ii) High mission costs
   

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5. Ideal Vision of Science Discovery in Remote Planets
   Solution: Fast science discovery with AI-based full autonomy
   5
   (v) Can deal with:
   Unknown Unknowns
   High Frequency
   Low Frequency
   (i) Fast science discovery
   (ii) Low mission costs
   (iv) Does scale
   (iii) Low risks
   

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6. Europa Lander Mission
   Image Credit: NASA/JPL 6
   

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7. Ocean worlds are the best places
   to search for life.
   7
   Image Credit: NASA
   

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8. 8
   Image Credit: NASA
   Europa’s surface has many features
   of interest for a science mission.
   

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9. 9
   Image Credit: NASA/JPL-Caltech
   

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10. Autonomy and Robotics
    Image Credit: EuroScience 10
    

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11. Autonomy Module: Design
    11
    • MAPE-K Loop based design
    • Machine learning driven quantitative
    planning and adaptation
    Monitor
    Analyze Plan
    Execute
    Knowledge
    System Under Test
    (NASA Lander)
    Autonomy
    

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12. Autonomy Module: Planner
    12
    PRISM Model
    Generator
    Policy
    Generator
    (PRISM)
    Plan
    Extractor
    Plan
    Translator
    Run-time
    Information
    PLEXIL
    Plan
    High-level
    Plan
    Policy File
    PRISM
    Model
    Planning
    Monitoring and and Analysis
    Excavation
    Locations
    Position
    (X, Y)
    Science
    Value
    Excavation
    Probability
    xloc1 (1.93, 0.2) 0.37 0.85
    xloc2 (1.51, -0.4) 0.41 0.83
    xloc3 (1.92, -0.3) 0.42 0.83
    …
    Dump Locations Position (X,Y)
    dloc1 (1.46, -0.9)
    dloc2 (1.49, 0.6)
    [select_xloc3, select_dloc2]
    Excavation: {
    Boolean planSuccess;
    LibraryCall GuardedMove (
    X = 1.92, Y = -0.3, Z = 0.05,
    DirX = 0, DirY = 0, DirZ = 1,
    SearchDistance = 0.25);
    planSuccess=Lookup(GroundFound);
    if (planSuccess) {
    LibraryCall Grind X = 1.92, Y = -0.3,
    Depth = 0.05, Length = 0.2, Parallel = true,
    GroundPos = Lookup(GroundPosition));
    planSuccess=Lookup(DiggingSuccess(0.83));
    if (planSuccess) {
    LibraryCall DigCircular (
    X = 1.92, Y = -0.3, Depth = 0.05,
    GroundPos = Lookup(GroundPosition),
    Parallel = true);
    LibraryCall Deliver (
    X = 1.49, Y = 0.6, Z = 0.5);
    } endif
    } endif
    }
    Execution
    

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13. Autonomy Module: Evaluation
    13
    Design
    • MAPE-K loop based design
    • Machine learning driven quantitative
    planning and adaptation
    Evaluation
    • Two testbeds: different fidelities
    & simulation flexibilities
    Monitor
    Analyze Plan
    Execute
    Knowledge
    System Under Test
    (NASA Lander)
    Autonomy
    Physical Testbed
    OWLAT
    (NASA/JPL)
    Virtual Testbed
    OceanWATERS
    (NASA/ARC)
    

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14. Physical Testbed at NASA/JPL
    Ocean World Lander Autonomy Testbed (OWLAT)
    14
    Physical Autonomy Testbed: https://www1.grc.nasa.gov/wp-content/uploads/2020_ASCE_OWLAT_20191028.pdf
    E2M Technologies six DOF Stewart Platform
    representing spacecraft lander
    Barrett WAM seven DOF arm mounted to
    lander with wrist FTS and tool changer
    Modular instruments to be mounted on robot arm
    Testbed setup and major components
    HITL simulator of lander and manipulator
    

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15. Virtual Testbed OceanWATERS by NASA ARC
    15
    Surface Model Lander Model
    Surface/Arm Interaction
    Material Property
    Visual Appearance
    Lighting Model
    Celestial Bodies
    Morphology
    Sensor Data: Galileo Orbit
    26, Europa Observation
    Fractal Terrain Generation • Analytical scoop model implemented for real time approximation
    • Particle simulation modeling: Discrete Element Method
    • Actuated Arm
    • Kinematic models
    • Dynamics and collision handling
    • Antenna: pan-tilt unit
    • Power and battery modeling
    • Tools: camera, scoop, grind
    Source of images: An Autonomy Software Testbed Simulation for Ocean Worlds Missions.
    Source Code of OceanWATERS Testbed: https://github.com/nasa/ow_simulator
    

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17. Homepage: https://nasa-raspberry-si.github.io/raspberry-si
    

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