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Outline

Outline. Deep Space One and Remote Agent Model-based Execution OpSat and the ITMS Model-based Reactive Planning Space Robotics. Modes Controlled Through Interactions. computer. bus control. remote terminal. driver. remote terminal. driver. How do we open a valve?.

marny-mccoy
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Outline

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  1. Outline • Deep Space One and Remote Agent • Model-based Execution • OpSat and the ITMS • Model-based Reactive Planning • Space Robotics

  2. Modes Controlled Through Interactions computer bus control remote terminal driver remote terminal driver How do we open a valve?

  3. Modes Controlled Through Interactions computer bus control remote terminal driver remote terminal driver Configuration goals MI MR Reactive Planner current state goal state Model Command

  4. Modes Controlled Through Interactions computer bus control remote terminal driver • Irreversible actions should be avoided by reactive planners. • Component schematics tend not to have loops • goals are serializable Solution: • Work goals from outputs to inputs. • Achieve goals serially. remote terminal driver

  5. Modes Controlled Through Interactions computer bus control remote terminal driver • Irreversible actions should be avoided by reactive planners. • Component schematics tend not to have loops • goals are serializable Solution: • Work goals from outputs to inputs. • Achieve goals serially. remote terminal driver

  6. Modes Controlled Through Interactions computer bus control remote terminal driver • Irreversible actions should be avoided by reactive planners. • Component schematics tend not to have loops • goals are serializable Solution: • Work goals from outputs to inputs. • Achieve goals serially. remote terminal driver

  7. vcmdin = vcmdout inflow = outflow cmd = reset Open On vcmdout(Driver) = cmd(Valve) cmd= on cmd = off cmd = open cmd = close cmd =off Closed Off Indirect controllability • Device states are not directly controllable

  8. Compilation vcmdin = vcmdout inflow = outflow • Remove intermediate variables using implicate generation • becomes a STRIPS-like problem • implicate generation done using conflict directed best first search cmd= reset Open On vcmdout(Driver) = cmd(Valve) driver = on cmd= open driver = on cmd = close cmd= on cmd = off Closed cmd =off Off

  9. Goal Open Closed Current idle driver = on cmd = close Open driver = on cmd = open idle Closed fail fail Stuck Concurrent policies • Convert resulting transition system into a local policy • Encoding is extremely compact • Ensure only reversible transitions are taken Open driver = on cmd = open driver = on cmd = close Closed

  10. Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  11. Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  12. cmd = on Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  13. cmd = on Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  14. cmd = close Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  15. Driver Fails! cmd = close Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  16. cmd = reset Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  17. cmd = close Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  18. cmd = off Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  19. Success! cmd = off Goal: off closed Cmd Goal Goal on off open closed Current Current cmd = off dr = on cmd=close on idle open idle off cmd = on idle dr = on cmd=open closed idle reset failure cmd=reset cmd=reset stuck fail fail

  20. Complexity of Reactive Planning • Worst Case per action: Depth * Sub-goal branch factor • Average Cost per action: Sub-goal branch factor Valve1 = open Valve2 = open Drv1 = off Drv2 = off Drv1 = on Drv2 = on CU= on CU= on CU= on CU= on CU= on CU= on

  21. Complexity Analysis Details • Average Cost per action: Subgoal branch factor • Avg cost = compute time / plan length • Each edge of the goal/subgoal tree is traversed twice.Compute time = 2 * E * B. • Each node of the goal / subgoal tree generates one action.plan length = N • Avg cost = O(E/N) • In the worst case E < = 2 * N. • Average Cost per action = O(B* N/N) = O(B) Subgoals

  22. Outline • Deep Space One and Remote Agent • Model-based Execution • OpSat and the ITMS • Model-based Reactive Planning • Space Robotics

  23. Current Livingstone Testbeds

  24. TechSat21 & Gflops Testbed MIT SSL Spheres MIT SSL Portable Satellite Assistant Ames

  25. Conclusions • Space is opening its doorway to a new generation of agile, highly independent explorers. • To survive decades of operation they must orchestrate complex regulatory and immune systems. • Model-based autonomy supports rapid prototyping through model-based programming and executives. • A model-based executive is a kind of stochastic optimal controller that makes extensive use of deduction to observe and control on the fly. • The core is OPSAT, a real-time, combinatorial optimization algorithm with logical feasibility constraints. • Fast, model-based reactive planning is achieved through knowledge compilation and by exploiting structural and safety constraints.

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