selection and monitoring of rover navigation modes: a probabilistic diagnosis approach
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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. - PowerPoint PPT PresentationTRANSCRIPT
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Selection and Monitoring of Rover Navigation modes:
A Probabilistic Diagnosis Approach
Thierry Peynot and Simon Lacroix
Robotics and AI groupLAAS/CNRS, Toulouse
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A great success story
Opportunity traverse
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Opportunity traverseApril 26th, 2005
A great success story
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Problem statement
1. Prevent (or at least detect) mobility faults
2. Recover from faulty situations
A diagnosis problem
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Various navigation modalities
• Large variety of environments: need for adaptation
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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 !
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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
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Outline
• Problem statement and approach
• Context information
• On-line monitoring
• Setting up the probabilistic network
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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
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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
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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
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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
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Outline
• Problem statement and approach
• Context information
• On-line monitoring
• Setting up the probabilistic network
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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)
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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)
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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)
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Monitor 2 : FlatTerrain assesment
FlatNav mode: simple arc trajectories generated on an obstacle map
• Analysis of the attitude angles measured by the IMU
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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
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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
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
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Other possible monitors
Visual servoing modes (trail following)
Stability margin analysis
Analysis of various localisation estimates (odoMetry, visOdom, Inertial navigation…)
And many others…
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Outline
• Problem statement and approach
• Context information
• On-line monitoring
• Setting up the probabilistic network
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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)
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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
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From monitor signatures to probabilities
Locomotion efficiency monitor: bayesian classification, OK
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From monitor signatures to probabilities
Locomotion efficiency monitor: bayesian classification, OK
FlatTerrain assesment
Pseudo-probabilities“conformation”
Signature
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From monitor signatures to probabilities
Locomotion efficiency monitor: bayesian classification, OK
FlatTerrain assesment
Attitude assesment
Pseudo-probabilities“conformation”
Signature
Pseudo-probabilities“conformation”
Signature
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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
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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
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Take home message
Navigation diagnosis is essential
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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
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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])