Selection and Monitoring of Rover Navigation modes:
A Probabilistic Diagnosis Approach
Thierry Peynot and Simon Lacroix
Robotics and AI groupLAAS/CNRS, Toulouse
A great success story
Opportunity traverse
Opportunity traverseApril 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
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Various navigation modalities
« 2D » mode « 3D » mode Road 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)
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Plus: the STOP mode !
Overview of the approach
• The robot is endowed with k navigation modes mk
• 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
Disretization Probabilistic 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
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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
Setting up the probabilistic network
Network of state transition probabilities
Observation Model(Context Information)
Conditional Dynamic Model(Transition Probabilities)
Conditional Probability (that mode mk 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 arepossible
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])