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© Mission Control Space Services Inc. 2018
Autonomous Soil Assessment System: A data-driven approach to planetary mobility hazard
detectionK. Raimalwala, M. Faragalli and E. Reid*
Operations, Autonomy, Intelligence
NASA-JPL
Mission Control Space Services was established in 2015 with the goal of developingsoftware solutions to operate and automate space robotic assets – both government andprivate. Combining expertise in space robotics, planetary science, machine learning andmachine vision to create new value in downstream space exploration applications.
April 26, 2018 3
• Lean start-up of multidisciplinary space professionals• Strong heritage in space flight mission operations, academic research,
analogue deployments and rover technology development for CSA• Staff of 12 plus external advisors• Located on Carleton University campus with strong research ties to aerospace
engineering department
Operations, Autonomy, Intelligence
© Mission Control Space Services Inc. 2018
© Mission Control Space Services Inc. 2018
Planetary Mobility Challenges
NASA/JPL - https://mars.nasa.gov/insight/multimedia/videos/?v=147
© Mission Control Space Services Inc. 2018
Planetary Mobility Challenges
Autonomous Soil Assessment System
Detect Hazards [Non-Geometric]
Real-Time, Data-Driven
Software Payload
RoverAgnostic
NASA-JPL
© Mission Control Space Services Inc. 2018
© Mission Control Space Services Inc. 2018
Training Phase
Stereocam
Subcell (100x100 pixels)
Gridcell(1.5m X 1.5m)
Texture descriptors
Terrain classifier database
User’s label
Visual Odometry
Wheel Encoders
IMU
Wheel Odometry
Rover Slip
Rover Pitch
Hazard Classification
Heuristics
Terramechanics models per terrain type
Slip
Pitch
xx x
xx
xx
x
x
Terramechanics model
Depth processing
OutputsSensors
© Mission Control Space Services Inc. 2018
Prediction Phase
Stereocam
Subcell (100x100 pixels)
Terrain class; Confidence
Texture descriptors
Terrain classifier database
Terramechanics models per terrain type
Depth processing
PredictionsSensors
K Nearest Neighbors
Weighted sum for sub-cells
Gridcellterrain
type
Gridcell(1.5m X 1.5m)
Slip
Hazard
Hazard prediction
X Input DataO Predicted Slip
High
Medium
Low
Slope estimation
Demo at Canadian Space Agency
© Mission Control Space Services Inc. 2018
© Mission Control Space Services Inc. 2018
White Sands National Monument
Staging Area
© Mission Control Space Services Inc. 2018
© Mission Control Space Services Inc. 2018
Terramechanics Models
Loose unconsolidated sand
Top: Loose unconsolidated sand
Mid: Firm unconsolidated sand
Bot: Cemented consolidated sand
© Mission Control Space Services Inc. 2018
Terramechanics Models
Firm unconsolidated sand
Top: Loose unconsolidated sand
Mid: Firm unconsolidated sand
Bot: Cemented consolidated sand
© Mission Control Space Services Inc. 2018
Terramechanics Models
Cemented consolidated sand
Top: Loose unconsolidated sand
Mid: Firm unconsolidated sand
Bot: Cemented consolidated sand
© Mission Control Space Services Inc. 2018
Terrain Classifier Performance
• 2 Sand types considered
• Loose and firm unconsolidated
sand were visually
indistinguishable – even by
humans!
• Gravel, rocky and sand also
successful in past test
campaigns at CSA mars yard
Predicted
N = 6106 Consolidated Unconsolidated
Act
ual Consolidated 1764 680
Unconsolidated 259 3403
Test Samples 6106
Accuracy 85%
Misclassification Rate
15%
Precision 83%
0
500
1000
1500
2000
2500
3000
3500
4000
Actual Consolidated Actual Unconsolidated
Predicted Consolidated Predicted Unconsolidated
© Mission Control Space Services Inc. 2018
Slip and Hazard Prediction
Performance
© Mission Control Space Services Inc. 2018
High-Slip Bias Demo 1
© Mission Control Space Services Inc. 2018
High-Slip Bias Demo 2 [sled test]
© Mission Control Space Services Inc. 2018
Future Work
• Terrain classification– Thermal IR imagery
– Deep learning (Convolutional Neural Networks)
• Terramechanics– Heterogeneous terrain
– Wheel-scale prediction
– Add more dimensionality• wheel sinkage from a
wheel-facing hazcam
Operations, Autonomy, Intelligence
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