Earth and Planetary Remote Sensing Laboratory ......driven approach to planetary mobility hazard detection K. Raimalwala, M. Faragalli and E. Reid* Operations, Autonomy, Intelligence

© 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




Planetary Mobility Challenges

NASA/JPL - https://mars.nasa.gov/insight/multimedia/videos/?v=147


Planetary Mobility Challenges

Autonomous Soil Assessment System

Detect Hazards [Non-Geometric]

Real-Time, Data-Driven

Software Payload

RoverAgnostic

NASA-JPL



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


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



White Sands National Monument

Staging Area



Terramechanics Models

Loose unconsolidated sand

Top: Loose unconsolidated sand

Mid: Firm unconsolidated sand

Bot: Cemented consolidated sand



Firm unconsolidated sand






Cemented consolidated sand





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


Slip and Hazard Prediction

Performance


High-Slip Bias Demo 1


High-Slip Bias Demo 2 [sled test]


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


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Documents

Earth and Planetary Remote Sensing Laboratory ......driven approach to planetary mobility hazard detection K. Raimalwala, M. Faragalli and E. Reid* Operations, Autonomy, Intelligence