improving lunar surface science with robotic recon · 2008. 11. 26. · david kring, essam heggy,...

Ames Research Center

Terry Fong, Matt Deans, Pascal Lee, Jen Heldmann, David Kring, Essam Heggy, and Rob Landis

Improving Lunar Surface Science with Robotic Recon

Apollo Lunar Surface Science

Jack Schmitt & LRV(Apollo 17)

Jack Schmitt & LRV(Apollo 17)

3Robotic Recon for Lunar Surface Science

What’s Changed Since Then?


What if the LRV had been a robot?

0

100

200

300

400

500

Cu

mu

lati

ve D

ays

on

Su

rface

1 1 0 1 3 3 3

12 days(crew on surface)


500 days(robots on surface)


By the end of Apollo, we could have had

40x more surface days

By the end of Apollo, we could have had

40x more surface days

- Surface Days#


Notional Lunar Campaign


First Three Years





During the first three years,crew is on the surface

< 10% of the time

During the first three years,crew is on the surface

< 10% of the time


Robotic Recon

Advance field work• Reduce unproductive crew time

(driving, navigating, searching)• Advance scouting (station-based)• Systematic survey (transect-based)

Surface data vs. orbital data • Higher resolution• Oblique & close-up views (non-nadir)• Contact & subsurface measurements

Robots with science instruments• Cameras, lidar, spectrometers,

penetrometers, etc.• Ground control with a science team• Robot is not the primary instrument

(this is not MER!)


Why Is Recon Useful?

Shorty Crater


Iterative Traverse Planning & Execution

GroundControlTeam

Robot

GroundData

Systems

Surfacedata

ScienceBackRoom

Crew

GroundData

Systems

Initial Planning Traverse

BaselineTraverse Plan

Robotic Recon

UpdatedTraverse Plan

ScienceobjectivesEVA plans

Orbital data

3D terrainmodel

ScienceTeam

Robottraverse

plans


Example Orbital Data

Digital Elevation Model (40 m/post)

Visible Image Base Map (10 m/pixel)

3D view of DEM + ortho image

Data source: Apollo Metric & Panoramic cameras(high-res scans by M. Robinson / ASU)

Processing: NASA Ames StereoPipelineRegistration: ULCN2005

Apollo 17Apollo 15

Apollo 15


Example Surface Recon Data

High-res panoramic image (140x68 deg, 21K x 16K pixels)

Terrain image (70 microns / pixel) Ground-penetrating radar vertical profile


Moses Lake Field Test (June 2008)• Simulate surface activities for

future missions (reduce risk)• Study lunar science operations

(not analog lunar science)• Use terrestrial science for

operational relevance


K10’s with 3D lidar, GPR, pancam, micro-imager

Science Team at JSC

Robotic recon at Moses Lake

Science Ops Study @ Moses LakeRobotic recon

• First phase of exploration field work• Supplement and complement remote

sensing (orbital) data• Better target crew activity

Moses Lake Field Test• K10 robots with science instruments• Experimental ground control at JSC• Use recon data for traverse planning

(survey site before crew arrives)

Test objectives • Improve understanding of how robotic

recon differs from robotic exploration• Develop ops protocol for robotic recon• Assess system performance and

communication patterns


Science Ops Study Team

PI Terry Fong (ARC)Co-I Matt Deans (ARC)

Science PI Pascal Lee (Mars Institute)Deputy Science PI Jen Heldmann (ARC)

PEL’s & field obs Dean Eppler, Brent Garry, Fred Hörz,Gary Lofgren, Jim Rice, Melissa Rice, Jeff Tripp

Scout robot PI David Kring (LPI)Survey robot PI Essam Heggy (LPI)

EVA suits Joe Kosmo (JSC) & Barbara Romig (JSC)

Mission operations Steve Riley (JSC) & Tifanie Smart (JSC)

Assessment Jen Rochlis (JSC) & Estrellina Pacis (SPAWAR)

Advisors Rob Ambrose, Doug Craig, & Kelly Snook

Sci

ence

Tea

m


Panoramic camera3D scanning lidarMicroscopic terrain imager

ScienceInstruments

Geologic scoutingFunction

CircuitousPath

Triage sample locationsIdentify particle distributionAssess surface compositionEvaluate depositional history

ScienceObjectives

ExplorationMode

robot crew

Robotic Recon

Advance scout• Initial phase of exploration field work

(identify & high-grade sites of interest)• Station-based assessment

(ground-level data)• Improve geologic understanding of site

K10 Red at Moses Lake Sand Dunes


Visible imager(s) Ground-penetrating radarMicroscopic terrain imager

ScienceInstruments

Characterize subsurfaceFunction

Systematic coveragePath

Map subsurface structureIdentify particle distributionAssess site stratigraphyIdentify water table depth

ScienceObjectives

MappingMode

robot

Robotic Recon

Systematic survey• Dense coverage (e.g., parallel-line transects) • Highly-repetitive measurements

(unproductive for crew to acquire)• Mapping sensors: visible & non-visible

(with acquisition constraints)

K10 Black at Moses Lake Sand Dunes


Experimental Ground Control

QuickTime™ and a decompressor

are needed to see this picture.


are needed to see this picture.Flight DirectorFlight Control Team

Robot PI

Science Operations TeamRobot Team

Robot Sys Lead

Robot TeamRep

DataCuration

PEL 1

PEL 2

Hardware Eng.

Power Eng.

ScienceDirector

Ground Data Sys



Robot

Robot Driver

ExecutionSecs to Hours

TacticalMinutes to Hours

StrategicMinutes to Days

Core Sci Team

Telemetry Eng.



EV1



EV2

“ Cap Com ” Science Officer

Science FlightLiason

Robot Officer

Robot Flight Liason

Downlink Lead



GroundControlTeam

Robot

GroundData

Systems

Surfacedata

ScienceBackRoom

Crew

GroundData

Systems



Robotic Recon



Orbital data

3D terrainmodel

ScienceTeam

Robottraverse

plans


Candidate Traverse Sites @ Moses Lake



GroundControlTeam

Robot

GroundData

Systems

Surfacedata

ScienceBackRoom

Crew

GroundData

Systems



Robotic Recon



Orbital data

3D terrainmodel

ScienceTeam

Robottraverse

plans


Robot Traverse Planning Tool

Data acquisition tasksData acquisition tasks

List view of traverse planList view of traverse plan

Map view of traverse planMap view of traverse plan

Timeline view of traverse planTimeline view of traverse plan


Recon Robot


Recon Instruments

3D scanning lidar• 3D topography

measurements• 5mm @ 500m• >2x resolution of

LRO LOLA

Color PanCam• Oblique, wide-angle

context views• 60x135 deg• >2x resolution of

LRO LROC-NA

Microscopic Imager• High-res, close-up

terrain views• 72 micron / pixel• >7,000x resolution of

LRO LROC-NA


PanCam (Site 1)

140 x 68 deg (H x V)6 tiles (each 10 Mpix: 3648x2736)

140 x 68 deg (H x V)6 tiles (each 10 Mpix: 3648x2736)

mottled

dune crests albedovariations

slightlyundulatingterrain

ripples


MI : Light Albedo Terrain (Site 1)

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500

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< 1m

m1-

1.5m

m1.

5-2m

m2-

2.5m

m2.

5-3m

m

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

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500

1000

1500

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2500

< 1m

m1-

1.5m

m1.

5-2m

m2-

2.5m

m2.

5-3m

m

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

0%

5%

10%

15%

20%

25%< 1

mm

1-1.

5mm

1.5-

2mm

2-2.

5mm

2.5-

3mm

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

0%

5%

10%

15%

20%

25%< 1

mm

1-1.

5mm

1.5-

2mm

2-2.

5mm

2.5-

3mm

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

poorly sorted, anglular grainssizes range from < 1mm to 2.6cm

poorly sorted, anglular grainssizes range from < 1mm to 2.6cm

Analysis by M. Rice (Cornell)


MI : Dark Albedo Terrain (Site 1)

0

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800

900

1000

< 1m

m

1-1.

5mm

1.5-

2mm

2-2.

5mm

2.5-

3mm

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

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1000

< 1m

m

1-1.

5mm

1.5-

2mm

2-2.

5mm

2.5-

3mm

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

0%

5%

10%

15%

20%

25%

30%

35%< 1

mm

1-1.

5mm

1.5-

2mm

2-2.

5mm

2.5-

3mm

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

0%

5%

10%

15%

20%

25%

30%

35%< 1

mm

1-1.

5mm

1.5-

2mm

2-2.

5mm

2.5-

3mm

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

well sorted, well rounded grainsthe majority are 1 to 1.5 mm

well sorted, well rounded grainsthe majority are 1 to 1.5 mm



MI : Mottled Terrain (Site 1)

0%

5%

10%

15%

20%

25%

30%

35%

40%< 1

mm

1-1.

5mm

1.5-

2mm

2-2.

5mm

2.5-

3mm

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

0%

5%

10%

15%

20%

25%

30%

35%

40%< 1

mm

1-1.

5mm

1.5-

2mm

2-2.

5mm

2.5-

3mm

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

0

200

400

600

800

1000

1200

1400

1600

1800

< 1m

m1-

1.5m

m1.

5-2m

m2-

2.5m

m2.

5-3m

m

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

0

200

400

600

800

1000

1200

1400

1600

1800

< 1m

m1-

1.5m

m1.

5-2m

m2-

2.5m

m2.

5-3m

m

3-4m

m4m

m-1

cm

1-2c

m

> 2cm

particle size

well sorted, well rounded grainssizes similar to dark albedo

well sorted, well rounded grainssizes similar to dark albedo




GroundControlTeam

Robot

GroundData

Systems

Surfacedata

ScienceBackRoom

Crew

GroundData

Systems



Robotic Recon



Orbital data

3D terrainmodel

ScienceTeam

Robottraverse

plans


EVA Planning

Approach• Robotic recon identifies & priorities sites of interest• Plan EVA traverse & activities to maximize crew productivity• Produce briefing package for crew (task map, cuff checklist, etc.)

EVA suit testing at Moses Lake


EVA Planning

M. Deans, B. Garry, J. Heldmann, G. Lofgren, D. Kring, P. Lee, and others …


Task Map (Site 1)

Station 1(Light Albedo)S + D

Station 2(Dark Albedo)S + D/T

Station 3(Mottled Albedo)S

S = Surface SampleD = Deep SampleT = TrenchD/T = Deep or Trench


EVA Traverse (Site 1)

Video view from Crew RoverVideo view from Crew Rover

Science Backroom and CapComScience Backroom and CapCom


Current Work

Robotic Recon “Ops Sim” (November 3-6 @ NASA Ames)• Test revised science ops protocol• Test ground data system improvements• Refine assessment metrics & procedures

Science Team• Pascal Lee, Mark Helper,

Kip Hodges, Jack Schmitt• Julie Chittenden (microimager),

Melissa Rice (pancam),Jeff Tripp (lidar)

Flight Control Team• Rob Landis, Steve Riley,

Tifanie Smart• Matt Deans, Leslie Keely,

Eric Park, Hans Utz


Ground Data System

Viz ExplorerViz Explorer

Image GalleryImage GalleryTeleop UITeleop UIRobot StatusRobot Status

Google Earth OpsGoogle Earth Ops


Experimental Flight Control Team

Intelligent Robotics GroupIntelligent Systems Division

NASA Ames Research Center

[email protected]

improving lunar surface science with robotic recon · 2008. 11. 26. · david kring, essam heggy,...

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