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 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])