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Robotic Space Explorers: To Boldly Go Where No AI System Has Gone Before

Robotic Space Explorers: To Boldly Go Where No AI System Has Gone Before. A Story of Survival 16.412J/6.834J September 19, 2001. Readings and Assignment. Model-based Agents:

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Robotic Space Explorers: To Boldly Go Where No AI System Has Gone Before

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  1. Robotic Space Explorers:To Boldly Go Where No AI System Has Gone Before A Story of Survival 16.412J/6.834J September 19, 2001

  2. Readings and Assignment Model-based Agents: • Remote Agent: to Boldy Go Where No AI System Has Gone Before,N.Muscettola, P. Nayak, B. Pell and B. Williams, Artificial Intelligence 103 (1998) 5-47. Partial Order Planning (for next lecture) • AIMA Chapter 11, Chapter 10, section on unification algorithm. For Problem Set 3: • Path Planning Using Lazy PRM,R. Bohlin and L. Kavraki, ICRA 2000.

  3. Outline • Motivation • Model-based autonomous systems • Remote Agent Example

  4. A Capable Robotic Explorer: Cassini Faster, Better, Cheaper • 150 million $ • 2 year build • 0 ground ops • 7 year cruise • ~ 150 - 300 ground operators • ~ 1 billion $ • 7 years to build Cassini Maps Titan courtesy JPL

  5. courtesy JPL ``Our vision in NASA is to open the Space Frontier . . . We must establish a virtual presence, in space, on planets, in aircraft and spacecraft.’’ - Daniel S. Goldin, NASA Administrator, May 29, 1996

  6. Four launches in 7 months Mars Climate Orbiter: 12/11/98 Mars Polar Lander: 1/3/99 QuickSCAT: 6/19/98 Stardust: 2/7/99 courtesy of JPL

  7. Miscommanded: • Mars Climate Orbiter • Clementine courtesy of JPL Spacecraft should be embodied with a survival instinct

  8. Vanished: • Mars Polar Lander • Mars Observer courtesy of JPL Spacecraft require commonsense…

  9. Traditional spacecraft commanding

  10. Quintuple fault occurs (three shorts, tank-line and pressure jacket burst, panel flies off). Mattingly works in ground simulator to identify new sequence handling severe power limitations. Mattingly identifies novel reconfiguration, exploiting LEM batteries for power. Swaggert & Lovell follow novel procedure to repair Apollo 13 lithium hydroxide unit. Houston, We have a problem ... courtesy of NASA

  11. What Makes this Difficult: Cassini Case Study courtesy JPL

  12. Reasoning through interactions is complex

  13. Reconfiguring for a Failed Engine Oxidizer tank Fuel tank

  14. Reconfiguring for a Failed Engine Oxidizer tank Fuel tank Open four valves

  15. Reconfiguring for a Failed Engine Oxidizer tank Fuel tank Open four valves Valve fails stuck closed

  16. Reconfiguring for a Failed Engine Oxidizer tank Fuel tank Open four valves Valve fails stuck closed Fire backup engine

  17. Challenge: Thinking Through Interactions Programmers must reason through system-wide interactions to generate codes for: • command confirmation • goal tracking • detecting anomalies • isolating faults • diagnosing causes • hardware reconfig • fault recovery • safing • fault avoidance • control coordination Equally problematic at mission operations level

  18. Outline • Motivation • Model-based autonomous systems • Remote Agent Example

  19. Model-based Autonomy • Programmers generate breadth of functions from commonsense models in light of mission goals. • Model-based Programming • Program by specifying commonsense, compositional declarative models. • Model-based Planning & Execution • Provide services that reason through each type of system interaction from models. • on the fly reasoning requires significant search & deduction within the reactive control loop.

  20. Quintuple fault occurs (three shorts, tank-line and pressure jacket burst, panel flies off). Mattingly works in ground simulator to identify new sequence handling severe power limitations. Mattingly identifies novel reconfiguration, exploiting LEM batteries for power. Swaggert & Lovell work on Apollo 13 emergency rig lithium hydroxide unit. Styles of Thinking Through Interactions courtesy of NASA

  21. Quintuple fault occurs (three shorts, tank-line and pressure jacket burst, panel flies off). Mattingly works in ground simulator to identify new sequence handling severe power limitations. Mattingly identifies novel reconfiguration, exploiting LEM batteries for power. Swaggert & Lovell work on Apollo 13 emergency rig lithium hydroxide unit. Styles of Thinking Through Interactions • Multiple fault diagnosis of unexperienced failures. • Mission planning and scheduling • Hardware reconfiguration • Scripted execution

  22. Example of a Model-based Agent: Goals Scripts • Goal-directed • First time correct • projective • reactive • Commonsense models • Heavily deductive Remote Agent Scripted Executive Mission Manager Planner/ Scheduler Diagnosis & Repair Mission-level actions & resources component models

  23. Conventional Wisdom: Reservations about Intelligent Embedded Systems • “[For reactive systems] proving theorems is out of the question” [Agre & Chapman 87]

  24. Many problems aren’t so hard

  25. How can general deduction achieve reactive time scales? Candidates withIncreasing cost SAT Solutions Generate Non-conflicting Successor Explanation for Conflicts Developed RISC-like, deductive kernel (OPSAT)

  26. Can model-based agents perform many different types of reasoning from a common model? Valve Transition Systems + Constraints + Probabilities Stuck open 0.01 Open 0. 01 Open Close 0. 01 Stuck closed Closed 0.01 inflow = outflow = 0

  27. Outline • Motivation • Model-based autonomous systems • Remote Agent Example

  28. Remote Agent Scripted Executive Mission Manager Planner/ Scheduler Diagnosis & Repair Remote Agent Architecture Ground System RAX_START RAX_START Real-Time Execution RAX Manager Flight H/W Fault Monitors Planning Experts (incl. Navigation)

  29. Executive requests plan Remote Agent Ground System Scripted Executive Mission Manager RAX_START RAX_START Real-Time Execution Planner/ Scheduler Diagnosis & Repair RAX Manager Flight H/W Fault Monitors Planning Experts (incl. Navigation)

  30. Mission manager establishes goals, planner generates plan Remote Agent Ground System Scripted Executive Mission Manager RAX_START RAX_START Real-Time Execution Planner/ Scheduler Diagnosis & Repair RAX Manager Flight H/W Fault Monitors Planning Experts (incl. Navigation)

  31. Executive executes plan Remote Agent Ground System Scripted Executive Mission Manager RAX_START RAX_START Real-Time Execution Planner/ Scheduler Diagnosis & Repair RAX Manager Flight H/W Fault Monitors Planning Experts (incl. Navigation)

  32. Diagnosis system monitors and repairs Remote Agent Ground System Scripted Executive Mission Manager RAX_START RAX_START Real-Time Execution Planner/ Scheduler Diagnosis & Repair RAX Manager Flight H/W Fault Monitors Planning Experts (incl. Navigation)

  33. Walk Through of Cassini Saturn Orbital Insertion courtesy JPL

  34. Plan for Next Time Horizon Remote Agent Ground System Scripted Executive Mission Manager RAX_START RAX_START Real-Time Execution Planner/ Scheduler Diagnosis & Repair RAX Manager Flight H/W Fault Monitors Planning Experts (incl. Navigation)

  35. Thrust Goals Power Attitude Engine

  36. Delta_V(direction=b, magnitude=200) Point(a) Mission Manager Sets Goals over Horizon Thrust Goals Power Attitude Off Engine Off

  37. Delta_V(direction=b, magnitude=200) Point(a) Planner Starts Thrust Goals Power Attitude Off Engine Off

  38. Delta_V(direction=b, magnitude=200) Point(a) Thrust Goals Power Attitude Thrust (b, 200) Off Engine Off

  39. Delta_V(direction=b, magnitude=200) Point(a) Off Thrust Goals Power Attitude Thrust (b, 200) Engine Off

  40. Delta_V(direction=b, magnitude=200) Point(a) Thrust (b, 200) Off Thrust Goals Power Attitude Engine Off

  41. Delta_V(direction=b, magnitude=200) Point(a) Thrust (b, 200) Off Thrust Goals Power Attitude Engine Off

  42. Delta_V(direction=b, magnitude=200) Point(a) Thrust (b, 200) Off Thrust Goals Power Point(b) Attitude Engine Off

  43. Delta_V(direction=b, magnitude=200) Point(a) Off Thrust Goals Power Point(b) Attitude Thrust (b, 200) Engine Off

  44. Thrust Goals Delta_V(direction=b, magnitude=200) Power Point(b) Point(a) Attitude Thrust (b, 200) Off Engine Off Warm Up

  45. Delta_V(direction=b, magnitude=200) Point(a) Point(b) Turn(b,a) Off Thrust Goals Power Attitude Thrust (b, 200) Engine Off Warm Up

  46. Delta_V(direction=b, magnitude=200) Turn(b,a) Point(a) Point(b) Off Thrust Goals Power Attitude Thrust (b, 200) Engine Off Warm Up

  47. Delta_V(direction=b, magnitude=200) Point(b) Turn(b,a) Point(a) Turn(a,b) Off Thrust Goals Power Attitude Thrust (b, 200) Engine Off Warm Up

  48. Delta_V(direction=b, magnitude=200) Point(a) Point(b) Turn(b,a) Turn(a,b) Off Thrust Goals Power Attitude Thrust (b, 200) Engine Off Warm Up

  49. Delta_V(direction=b, magnitude=200) Point(a) Point(b) Turn(b,a) Turn(a,b) Off Plan Completed! Thrust Goals Power Attitude Thrust (b, 200) Engine Off Warm Up

  50. Delta_V(direction=b, magnitude=200) Plan Model Fragment Used Thrust Goals Power contains Attitude Thrust (b, 200) Engine

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