Fault Detection in Autonomous Assembly by Space Robot Using Semantic Task Model - PowerPoint PPT Presentation

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Fault Detection in Autonomous Assembly by Space Robot Using Semantic Task Model

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  1. Fault Detection in Autonomous Assembly by Space Robot Using Semantic Task Model ISTS 2006 - s - 02 Keita Sawayama Dept. of Aeronautics & Astronautics, The University of Tokyo

  2. Contents • Background • Our Approach: Use of Semantic Information • System Architecture • Simulation Experiment • Conclusions

  3. Background

  4. Future Space System • Sustainable Space System • Orbital Recycle & Reconfiguration • Assembly, Maintenance, Diagnosis Autonomous Space Robots Applications

  5. Autonomous Space Robots • Application to routine work • Ex. Small satellites orbital assembly small satellite • Rule-based control • Conventional, Established control approach

  6. Rule-based approach • Suitable for prescribed task sequences • Reliable execution IF THEN condition command Behavior Rule [move(x,y,z)]

  7. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6

  8. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6

  9. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6

  10. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6

  11. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6

  12. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6

  13. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6 Misaligned!!

  14. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6

  15. Rule-based approach • Suitable for prescribed task sequences • Reliable execution command 1 command 2 command 3 command 4 command 5 command 6 STOP! Uncertainties in real world tasks

  16. Rule-based approach • Suitable for prescribed task sequences • Reliable execution How to recover ? command 1 command 2 command 3 command 4 command 5 command 6 STOP! Uncertainties in real world tasks

  17. Problem of Rule-based approach • Not flexible to unexpected situations • Robots need more information for control IF THEN condition command Behavior Rule [move(x,y,z)] • Semantics of the action • Purpose, Relevant objects, • Focused relations, ・・・ Semantic Information Key to Flexibility

  18. Our Approach ~Use of Semantic Information~

  19. Our approach • Use of Semantic Information • Normal operations • Unexpected Situations Rule-based control Inference, Re-planning Use of semantic information

  20. Semantics of the behavior Purpose :“Locate” Object :“The blue block” Target :“Beside the yellow block” Semantic Information behind Command Command “move(0,0,-10)” = Rule-basedBehaviordescription + ・ Behavior understanding ・ Situation recognition Basis forrational inference & planning

  21. Example of Using S.I. Command “move(0,0,-10)” + Semantics of the behavior Purpose : “Locate” Object : “The blue block” Target : “Beside the yellow block” Behavior understanding “Locate the blue block beside the yellow block” Situation Recognition “Under satellite assembly situation”

  22. System Architecture

  23. Plan Representation Robot Plan Semantic Information (Annotation) Commands Similar to “Semantic Web” concept

  24. Control Sequence Normal Mode Command 1 Semantic Information 1 Command 2 Semantic Information 2 Command 3 Semantic Information 3

  25. Control Sequence Normal Mode Command 1 Semantic Information 1 Command 2 Semantic Information 2 Command 3 Semantic Information 3

  26. Control Sequence Normal Mode Command 1 Semantic Information 1 Command 2 Semantic Information 2 Command 3 Semantic Information 3

  27. Control Sequence Normal Mode Command 1 Semantic Information 1 Command 2 Semantic Information 2 Command 3 Semantic Information 3

  28. Control Sequence Error occurs Command 1 Semantic Information 1 Command 2 Semantic Information 2 Command 3 Semantic Information 3

  29. Control Sequence Recovery Mode Cause Inference Sensing Planning Command 1 Semantic Information 1 Sensing Action Command 2 Semantic Information 2 Cause Verification Command 3 Semantic Information 3 Recovery Planning Recovery Action

  30. Control Sequence Recovery Mode Cause Inference Sensing Planning Command 1 Semantic Information 1 Sensing Action Command 2 Semantic Information 2 Cause Verification Command 3 Semantic Information 3 Recovery Planning Recovery Action

  31. Modeling Method in STM

  32. Modeling Method in STM

  33. Modeling Method in STM Relation About Adding Force Force Adding Force Interferes Reduction of Moving Closer Moving Closer Obstacle Role Force By Obstacle By Obstacle Way N Possible Causes

  34. Modeling Method in STM Relation About Adding Force Force Adding Force Interfering Reduction of Moving Closer Moving Closer Obstacle Role Use of Semantic Task Model By Obstacle By Obstacle Way N Way of Function Achievement

  35. Simulation Experiment

  36. [Scenario 1] [Scenario 2] End Effector Downward Cell Upward Force Force Experiment Scenarios Can the system recognize the different influence of the same force? ⇒ Influence ?? ⇒ Influence ??

  37. Simulation Experiment

  38. The WFAs of “Accelerating moving farther” are ---By pushed by something ---By inertial force The WFAs of “Interfering moving closer” are ---By interfering moving path ---By failure in drive torque ---By mismatch in coordinate system ---By interrupting robot motion Result [Scenario 1] [Scenario 2] Same observation, Different interpretation The system can output different causes. Utilization of semantic information for fault detection and diagnosis

  39. Conclusions

  40. Conclusion • New space robot control architecture • Rule-based + Semantic Information • The system utilizes semantic information for handling unexpected events. • Fault detection and diagnosis scheme • The system can understand the situation and infer the cause of the problem flexibly and rationally. Application