Rushana Karimova 1 , Vikram Unnithan 1 , Laurenz Thomsen 1 , Jakob Schwendner 2 , Mathias Hökelmann 2 , Andrea Pacifici 3 , Riccardo Pozzobon 3 Angelo Pio Rossi 1 (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 Results Geomorphological Map 10 to 0.1 km Introduction Testing 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. Literature Marlow, 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