Ames Research Center Improving Lunar Surface Science with Robotic Recon
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1 Ames Research Center Improving Lunar Surface Science with Robotic Recon Terry Fong, Matt Deans, Pascal Lee, Jen Heldmann, David Kring, Essam Heggy, and Rob Landis
2 Apollo Lunar Surface Science Jack Schmitt & LRV (Apollo 17)
3 What s Changed Since Then? 3
4 What if the LRV had been a robot? 500 Cumulative Days on Surface # - Surface Days 500 days (robots on surface) By the end of Apollo, we could have had 40x more surface days 12 days (crew on surface)
5 Notional Lunar Campaign 5
6 First Three Years 1140 days (robots on surface) During the first three years, crew is is on the surface < 10% of the time 87 days (crew on surface) 6
7 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!) 7
8 Why Is Recon Useful? Shorty Crater 8
9 Iterative Traverse Planning & Execution Baseline Traverse Plan Updated Traverse Plan Initial Planning Robotic Recon Traverse Orbital data Robot Crew 3D terrain model Robot traverse plans Surface data Science objectives EVA plans Science Team Ground Control Team Ground Data Systems Science Back Room Ground Data Systems 9
10 Example Orbital Data Apollo 15 Apollo 17 Digital Elevation Model (40 m/post) Apollo 15 3D view of DEM + ortho image Visible Image Base Map (10 m/pixel) Data source: Apollo Metric & Panoramic cameras (high-res scans by M. Robinson / ASU) Processing: NASA Ames StereoPipeline Registration: ULCN
11 Example Surface Recon Data High-res panoramic image (140x68 deg, 21K x 16K pixels) Terrain image (70 microns / pixel) Ground-penetrating radar vertical profile 11
12 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 12
13 Science Ops Moses Lake Robotic 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 K10 s with 3D lidar, GPR, pancam, micro-imager Robotic recon at Moses Lake Science Team at JSC 13
14 Science Ops Study Team Science Team PI Co-I Science PI Deputy Science PI PEL s & field obs Scout robot PI Survey robot PI EVA suits Mission operations Assessment Advisors Terry Fong (ARC) Matt Deans (ARC) Pascal Lee (Mars Institute) Jen Heldmann (ARC) Dean Eppler, Brent Garry, Fred Hörz, Gary Lofgren, Jim Rice, Melissa Rice, Jeff Tripp David Kring (LPI) Essam Heggy (LPI) Joe Kosmo (JSC) & Barbara Romig (JSC) Steve Riley (JSC) & Tifanie Smart (JSC) Jen Rochlis (JSC) & Estrellina Pacis (SPAWAR) Rob Ambrose, Doug Craig, & Kelly Snook 14
15 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 Function Mode Path Science Instruments Science Objectives Geologic scouting Exploration Circuitous robot Panoramic camera 3D scanning lidar Microscopic terrain imager Triage sample locations Identify particle distribution Assess surface composition Evaluate depositional history crew 15
16 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 Function Mode Path Science Instruments Science Objectives Characterize subsurface Mapping robot Systematic coverage Visible imager(s) Ground-penetrating radar Microscopic terrain imager Map subsurface structure Identify particle distribution Assess site stratigraphy Identify water table depth 16
17 Experimental Ground Control Tactical Minutes to Hours QuickTime and a QuickTime and a decompressor decompressor are needed to see this picture. are needed to see this picture. Flight Control Team QuickTime and a decompressor are needed to see this picture. QuickTime and a decompressor Flight Director are needed to see this picture. QuickTime and a decompressor are needed to see this picture. Robot Driver Robot Officer Downlink Lead Cap Com Science Officer Robot Flight Liason Core Sci Team Science Flight Liason Robot Sys Lead Robot PI Science Director Hardware Eng. PEL 1 Robot Team Rep EV1 EV2 Robot Power Eng. Telemetry Eng. PEL 2 Data Curation Ground Data Sys Robot Team Science Operations Team Execution Secs to Hours Strategic Minutes to Days 17
18 Iterative Traverse Planning & Execution Baseline Traverse Plan Updated Traverse Plan Initial Planning Robotic Recon Traverse Orbital data Robot Crew 3D terrain model Robot traverse plans Surface data Science objectives EVA plans Science Team Ground Control Team Ground Data Systems Science Back Room Ground Data Systems 18
19 Candidate Traverse Moses Lake 19
20 Iterative Traverse Planning & Execution Baseline Traverse Plan Updated Traverse Plan Initial Planning Robotic Recon Traverse Orbital data Robot Crew 3D terrain model Robot traverse plans Surface data Science objectives EVA plans Science Team Ground Control Team Ground Data Systems Science Back Room Ground Data Systems 20
21 Robot Traverse Planning Tool Timeline view of traverse plan Data acquisition tasks List view of traverse plan Map view of traverse plan 21
22 Recon Robot 22
23 Recon Instruments 3D scanning lidar 3D topography measurements 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 23
24 PanCam (Site 1) mottled dune crests albedo variations slightly undulating terrain 140 x 68 deg (H x V) 6 tiles (each 10 Mpix: 3648x2736) ripples 24
25 MI : Light Albedo Terrain (Site 1) < 1mm 1mm 1-1.5mm 1-1.5mm 1.5-2mm 1.5-2mm 2-2.5mm 2-2.5mm 2.5-3mm 2.5-3mm 3-4mm 3-4mm 4mm-1cm 4mm-1cm 1-2cm 1-2cm > 2cm 2cm particle size 25% 20% 15% 10% 5% 0% < 1mm 1mm 1-1.5mm 1-1.5mm 1.5-2mm 1.5-2mm 2-2.5mm 2-2.5mm 2.5-3mm 2.5-3mm 3-4mm 3-4mm 4mm-1cm 4mm-1cm 1-2cm 1-2cm > 2cm 2cm poorly sorted, anglular grains sizes range from < 1mm to 2.6cm particle size Analysis by M. Rice (Cornell) 25
26 MI : Dark Albedo Terrain (Site 1) < 1mm 1mm 1-1.5mm 1-1.5mm 1.5-2mm 1.5-2mm 2-2.5mm 2-2.5mm 2.5-3mm 2.5-3mm 3-4mm 3-4mm 4mm-1cm 4mm-1cm 1-2cm 1-2cm > 2cm 2cm particle size 35% 30% 25% 20% 15% 10% 5% 5% 0% 0% < 1mm 1mm 1-1.5mm 1-1.5mm 1.5-2mm 1.5-2mm 2-2.5mm 2-2.5mm 2.5-3mm 2.5-3mm 3-4mm 3-4mm 4mm-1cm 4mm-1cm 1-2cm 1-2cm > 2cm 2cm well sorted, well rounded grains the majority are 1 to 1.5 mm particle size Analysis by M. Rice (Cornell) 26
27 2-2.5mm 2-2.5mm 2.5-3mm 2.5-3mm 3-4mm 3-4mm 4mm-1cm 4mm-1cm 1-2cm 1-2cm > 2cm 2cm 1.5-2mm 2-2.5mm 2-2.5mm 2.5-3mm 2.5-3mm 3-4mm 3-4mm 4mm-1cm 4mm-1cm 1-2cm 1-2cm > 2cm 2cm 1.5-2mm 1.5-2mm MI : Mottled Terrain (Site 1) % 40% 35% 35% 30% 30% 25% 25% 20% 20% 15% 15% 10% 10% 5% 5% 0% 0% particle size particle size 27 < 1mm 1mm 1-1.5mm 1-1.5mm 1.5-2mm < 1mm 1mm 1-1.5mm 1-1.5mm well sorted, well rounded grains sizes similar to dark albedo Analysis by M. Rice (Cornell)
28 Iterative Traverse Planning & Execution Baseline Traverse Plan Updated Traverse Plan Initial Planning Robotic Recon Traverse Orbital data Robot Crew 3D terrain model Robot traverse plans Surface data Science objectives EVA plans Science Team Ground Control Team Ground Data Systems Science Back Room Ground Data Systems 28
29 EVA Planning EVA suit testing at Moses Lake 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.) 29
30 EVA Planning M. Deans, B. Garry, J. Heldmann, G. Lofgren, D. Kring, P. Lee, and others 30
31 Task Map (Site 1) Station 1 (Light Albedo) S + D Station 2 (Dark Albedo) S + D/T S = Surface Sample D = Deep Sample T = Trench D/T = Deep or Trench Station 3 (Mottled Albedo) S 31
32 EVA Traverse (Site 1) Video view from Crew Rover Science Backroom and CapCom 32
33 Current Work Robotic Recon Ops Sim (November 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 33
34 Ground Data System Viz Explorer Google Earth Ops Robot Status Teleop UI Image Gallery 34
35 Experimental Flight Control Team 35
36 Intelligent Robotics Group Intelligent Systems Division NASA Ames Research Center
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