Rushana Karimova1, Vikram Unnithan1, Laurenz Thomsen1, Jakob Schwendner2, Mathias Hökelmann2, Andrea Pacifici3, Riccardo Pozzobon3 Angelo Pio Rossi1
(1) Jacobs University Bremen Jacobs University Bremen, Campus Ring 1, 28759 Bremen, (2) German Research Center for Artificial Intelligence (DFKI), Robert-Hooke-Straße 1, 28359 Bremen, (3)"G. d'Annunzio" University of Chieti-Pescara, Via dei Vestini, 66100 Chieti CH, Italy
1 to 100 cm
0.1 to 100 km
Results
Geomorphological Map10 to 0.1 km
IntroductionTesting rover equipment on analogue sites on Earth is
crucial for evaluating the rovers` performance on the soil
types which they will encounter on other planetary
bodies.
We tested the hybrid leg-wheel rover ASGUARD,
developed by the German Research Center for
Artificial Intelligence, during the ROBEX (Robotic
Exploration of Extreme Environments) field trip in June
2015, and collected drone imagery in June, 2016. The
test location was the Fossa cinder cone on the island of
Vulcano in Italy.
Objectives:
• Characterize the test site from mechanical planetary
analogue perspective
• Test and report rover performance on terrains of
various complexity
• Come up with a simple and reliable method to quantify
terrain roughness on the rover test sites
Summary
Fig. 1. ASGUARD rover with a mounted camera on the test site
Methods and Data Collection
Test drive terrain settings: boulder field - loose ash -
solid duricrust - dry riverbed - gully
A GoPro Hero4 camera recorded the path during each
drive. A mobile phone GPS tracked the positioning.
Aerial imagery: collected with an aerial drone over the
boulder field.
Large scale: Geomorphological map, using google
satellite imagery (OpenLayers plugin in QGIS).
Medium to small scale: 3D models, orthomosaics, DEMs
generated from rover`s video feed and drone images
in PhotoScan and Pix4D software.
Roughness: compared for all rover test settings by
manual particle counting from images collected on
site
Automatic particle counting: performed on the
orthomosaic from drone imagery in ImageJ
Rover performance: analyzed using the GPS track and
observations on site
3D models and roughness
• Model resolution: 2.13 cm/pixel (PhotoScan), 1cm/pixel (Pix4D)
• Aerial model and orthomosaic: extensive coverage and very fine
details
• Models from oblique (on-rover) images: fine details but limited
coverage
Particle count for roughness
estimation:
a) A subset of the orthophoto
generated in Pix4D
10 cm to 10 m Limitations and Future Work
b) A Mean Square Error Map
(MSE) derived from the DTM
c) Map of particles
counted in ImageJ
Fig. 2. Screenshot of the 3D model of the boulder field from aerial images
Rover Performance
Fig.4. Rover speed on tracks A,B, C in m/s vs. track points
Fig.3. Locations and slopes of
rover tracks A, B, and C on the
boulder field
• Rover video feed was not usable in many
cases due to the instability of the camera
and its rolling shutter, which produced
blurry images.
• Collecting aerial images on the other test
drive locations would allow for roughness
comparisons using the automated
counting.
• For drive tests, using a differential GPS
devise could improve positioning and
elevation data.
• Additional sensors on the rover (e.g.
engine current sensor) could refine the
performance data considerably.
LiteratureMarlow, J. J., Martins, Z., & Sephton, M. A. (2008). Mars on Earth: soil analogues for future Mars
missions. Astronomy & Geophysics, 49(2), 2-20.
Eich, M., Grimminger, F., Bosse, S., Spenneberg, D., & Kirchner, F. (2008). Asguard: A Hybrid-Wheel Security
and SAR-Robot Using Bio-Inspired Locomotion for Rough Terrain. In International Workshop on Robotics for
Risky Interventions & Surveillance of Environment, Benicàssim Spain.
Kanzog, C. (2015). ROBEX-Robotic Exploration of Extreme Environments. Journal of Unmanned System
Technology, 3(2), 40-45.
Turner, D., Lucieer, A., & Watson, C. (2012). An automated technique for generating georectified mosaics
from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM)
point clouds. Remote Sensing, 4(5), 1392-1410.
Ferreira, T., & Rasband, W. (2012). ImageJ user guide. IJ1. 46r. Natl. Inst. Health, Bethesda, MD.
http://rsb. info. nih. gov/ij/docs/guide/user-guide. pdf.
A
B
C
Track Slope (degrees) Speed (m/s)
A 25 0.276
B 25.6 0.251
C 12.5 0.448
Table1. Average slope and speed on tracks A,B,C
Flatter slopes →
higher speeds at
similar roughness
(track C).
Acknowledgements This work was supported by the Helmholtz Association,
project alliance ROBEX, under contract number HA-
304. We thank Sven Klüber for assisting with the drone
operation, Matthias Killian, Christian Bühler and Paulina
Prondzinsky for their help during field work
A
B
C
• Orthomosaics and models created in
Pix4D software from drone imagery
worked best
• Drone images → orthomosaic →
automatic particle counting in
imageJ most straightforward for
quantitative roughness
• Mean Square Error maps – boulder,
slope maps – channel related
roughness, use together for traverse
planning
• Minimum height of an obstacle that
could not be crossed =~25cm
• Slope - larger influence on
traversibility than roughness
• Loose soil + steep slope ~30°created too much wheel slippage
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