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

A great success story Opportunity traverse

April 26th, 2005 A great success story

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

Various navigation modalities Large variety of environments: need for adaptation

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

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 !

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

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

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

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

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

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

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

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)

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)

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)

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

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

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

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

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

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

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)

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

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

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

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

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

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

Take home message Navigation diagnosis is essential

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

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])