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Selection and Monitoring of Rover Navigation modes: A Probabilistic Diagnosis Approach

Selection and Monitoring of Rover Navigation modes: A Probabilistic Diagnosis Approach. Thierry Peynot and Simon Lacroix Robotics and AI group LAAS/CNRS, Toulouse. Opportunity traverse. A great success story. A great success story. Opportunity traverse April 26th, 2005.

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Selection and Monitoring of Rover Navigation modes: A Probabilistic Diagnosis Approach

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  1. Selection and Monitoring of Rover Navigation modes: A Probabilistic Diagnosis Approach Thierry Peynot and Simon Lacroix Robotics and AI group LAAS/CNRS, Toulouse

  2. Opportunity traverse A great success story

  3. A great success story Opportunity traverse April 26th, 2005

  4. Problem statement Prevent (or at least detect) mobility faults Recover from faulty situations • A diagnosis problem

  5. Various navigation modalities • Large variety of environments: need for adaptation

  6. (various other locomotion modes possible) … Various navigation modalities • Large variety of environments: need for adaptation • Various locomotion modes rolling mode « rolking » mode

  7. Various navigation modalities • Large variety of environments: need for adaptation • Various navigation modes (i.e. various instances of the perception / decision / action loop) Plus: reactive navigation, trail following, visual servoing, … « 2D » mode « 3D » mode Road following Plus: the STOP mode ! (Back to the MERs: Direct control, AutoNav, VisOdom)

  8. Probabilistic diagnosis approach: Network of state transition probabilities Overview of the approach • The robot is endowed with k navigationmodes mk • Problem: determine the best mode m* to apply, considering : • “Context” information related to the environment (a priori information) • Behavior information acquired on-line (thanks to “monitors”)

  9. Outline • Problem statement and approach • Context information • On-line monitoring • Setting up the probabilistic network

  10. 1. From initial data (aerial data, GIS…) traversability landmarks Navigation supports DTM / Orthoimage… … structured into navigation models Context information Requirement: an environment representation that expresses the applicability probabilities for each considered mode

  11. Global model update Context information Requirement: an environment representation that expresses the applicability probabilities for each considered mode 2. From data gathered by the robot : terrain classification Disretization Probabilistic classification

  12. Context information Requirement: an environment representation that expresses the applicability probabilities for each considered mode 3. From data gathered by the robot : DTM analysis Evaluation of robot placements on the DTM  DTM “Difficulty” index

  13. Fast 2D mode Forbidden Slow 3D mode Context information Requirement: an environment representation that expresses the applicability probabilities for each considered mode 4. From an analysis provided by the operators :

  14. Outline • Problem statement and approach • Context information • On-line monitoring • Setting up the probabilistic network

  15. Monitoring the behaviour Requirement: to evaluate the adequacy of the current applied mode • Principle: check perceived signatures wrt. a model of the mode • A monitor is dedicated to a given mode (generic monitors can be defined though)

  16.  supervised bayesian classification (3 states: no slippages, slippages, fault) Monitor 1 : locomotion efficiency For a 6 wheels rover: • Consistency between individual wheel speeds • Consistency between rover rotation speed estimates (odometry vs FOG gyro)

  17. Monitor 1 : locomotion efficiency For a 6 wheels rover: • Consistency between individual wheel speeds • Consistency between rover rotation speed estimates (odometry vs FOG gyro)  Associated state transition network (2 states: rolling, rolking, P(rolling) = 0.8)

  18. Monitor 2 : FlatTerrain assesment FlatNav mode: simple arc trajectories generated on an obstacle map • Analysis of the attitude angles measured by the IMU

  19. Monitor 3 : Attitude assessment on rough terrains RoughNav mode: trajectory selection on the basis of placements on the DTM • Comparison between the predicted and measured rover attitudes along the trajectory

  20. Monitor 3 : Attitude assessment on rough terrains RoughNav mode: trajectory selection on the basis of placements on the DTM • Comparison between the predicted and measured rover attitudes along the trajectory predicted measured

  21. Monitor 3 : Attitude assessment on rough terrains RoughNav mode: trajectory selection on the basis of placements on the DTM • Comparison between the predicted and measured rover attitudes along the trajectory Predicted vs. observed robot pitch angle

  22. Stability margin analysis Other possible monitors Visual servoing modes (trail following) Analysis of various localisation estimates (odoMetry, visOdom, Inertial navigation…) And many others…

  23. Outline • Problem statement and approach • Context information • On-line monitoring • Setting up the probabilistic network

  24. Observation Model (Context Information) Conditional Dynamic Model (Transition Probabilities) Conditional Probability (that mode mk should be applied) (O = context info, C = behavior monitors) Setting up the probabilistic network Network of state transition probabilities

  25. 3. Difficulty map   Difficulty [0,1] Pseudo-probability From context information to probabilities 1. Aerial images analysis: probabilistic classification, OK 2. Terrain classification from rover imagery: probabilistic classification, OK 4. Information given by the operator: to be conformed with probabilities

  26. From monitor signatures to probabilities Locomotion efficiency monitor: bayesian classification, OK

  27.  From monitor signatures to probabilities Locomotion efficiency monitor: bayesian classification, OK FlatTerrain assesment Signature “conformation” Pseudo-probabilities

  28.  Signature “conformation” Pseudo-probabilities From monitor signatures to probabilities Locomotion efficiency monitor: bayesian classification, OK FlatTerrain assesment Attitude assesment   Signature “conformation” Pseudo-probabilities

  29. Merging monitors and context information Example: • Two navigation modes: flatNav and roughNav (+ stop) • Context information: difficulty map computed on the DEM • Two monitors: flatTerrain and attitude assessment

  30. Merging monitors and context information Example: • Two navigation modes: flatNav and roughNav (+ stop) • Context information: difficulty map computed on the DEM • Two monitors: flatTerrain and attitude assessment

  31. Take home message Navigation diagnosis is essential

  32. Take home message Navigation diagnosis is essential From a research scientist perspective: • Reinforce links with the FDIR/Diagnosis community • Probabilistic diagnosis approaches seems appealing (but calls for lot of programmer expertise and tuning) • Consider integration with the overall rover decisional architecture From an engineer perspective: • Many simple ad hoc solutions are possible

  33. Back to Opportunity No discriminative context information… Two possible monitors: • Comparison of visOdom / odometry motion estimates • Surveillance of the current consumptions / wheel individual speeds (cf [OJEDA-TRO-2006])

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