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. A great success story Opportunity traverse

3. April 26th, 2005 A great success story

4. Problem statement 1. Prevent (or at least detect) mobility faults 2. Recover from faulty situations  A diagnosis problem

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

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

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

8. Overview of the approach The robot is endowed with k navigation modes m k Problem: determine the best mode m* to apply, considering : 1. “Context” information related to the environment (a priori information) 2. Behavior information acquired on-line (thanks to “monitors”) Probabilistic diagnosis approach: Network of state transition probabilities

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

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

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

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 DTM “Difficulty” index  Evaluation of robot placements on the DTM

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

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

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 measuredpredicted

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. Other possible monitors Visual servoing modes (trail following) Stability margin analysis 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. Network of state transition probabilities Observation Model (Context Information) Conditional Dynamic Model (Transition Probabilities) Conditional Probability (that mode m k should be applied) (O = context info, C = behavior monitors)

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

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 Pseudo-probabilities“conformation”  Signature

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

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])