ARIEL: Autonomous Excavation Site Selection for Europa Lander Mission Concept

This paper presents ARIEL (Autonomous Ranking and Interrogation of Excavation Location), an autonomy system for selecting an excavation site on-board for NASA's Europa Lander Mission Concept. Historically, excavation site selection has been performed by a lengthy ground-in-the-loop (GITL) process involving manual inspections, assessments, and decision making in past missions. However, as Europa Lander would have approximately 20 days of lifetime after the landing, many surface activities, including excavation site selection, must be autonomously performed on-board. This paper describes the overall system of ARIEL as well as its two major algorithmic components: vision-based candidate selection and smart interrogation, which estimates the physical properties of the icy surface through physical contact with the robotic arm's end-effector. Preliminary results are presented using images from Earth analogue sites. The Europa Lander mission returned to the formulation phases in early 2019 while ARIEL was at an early stage of development. Described in this paper is a snapshot of ARIEL as of the project suspension. This paper also describes the remaining challenges to be solved, should the mission resume in the future.

[1]  S. Grasby,et al.  Borup Fiord: A Unique Glacial Environment of Astrobiological Significance and Potential Analogue to Europa Exploration , 2006 .

[2]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Raymond E. Arvidson,et al.  Results from the Mars Phoenix Lander Robotic Arm experiment , 2009 .

[4]  Raymond E. Arvidson,et al.  Phoenix soil physical properties investigation , 2009 .

[5]  D. Sculley,et al.  Web-scale k-means clustering , 2010, WWW '10.

[6]  Masahiro Ono,et al.  SPOC: Deep Learning-based Terrain Classification for Mars Rover Missions , 2016 .

[7]  K. S. Tso,et al.  Lunar Surface Operation Testbed (LSOT) , 2012, 2012 IEEE Aerospace Conference.

[8]  T. Estlin,et al.  AEGIS autonomous targeting for ChemCam on Mars Science Laboratory: Deployment and results of initial science team use , 2017, Science Robotics.

[9]  Raymond E. Arvidson,et al.  The Phoenix Mars Lander Robotic Arm , 2009, 2009 IEEE Aerospace conference.

[10]  D. Gabor,et al.  Theory of communication. Part 1: The analysis of information , 1946 .

[11]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[12]  B. Cooper.  Driving on the Surface of Mars Using the Rover Control Workstation , 1998 .

[13]  M. Rosenblatt.  Remarks on Some Nonparametric Estimates of a Density Function , 1956 .

[14]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[15]  B. S. Manjunath,et al.  Color image segmentation , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[16]  Stefano Soatto,et al.  Quick Shift and Kernel Methods for Mode Seeking , 2008, ECCV.

[17]  David R. Thompson,et al.  AEGIS automated targeting for MER opportunity rover , 2012 .

[18]  Ashitey Trebi-Ollennu,et al.  In situ robotic arm operations , 2009, IEEE Robotics & Automation Magazine.

[19]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

[20]  Robert G. Deen,et al.  InSight Mars Lander Robotics Instrument Deployment System , 2018, Space Science Reviews.

[21]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[23]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[24]  R. Joshi,et al.  Autonomous Science Restart for the Planned Europa Mission with Lightweight Planning and Execution , 2017 .

[25]  Abhinandan Jain,et al.  A reusable, real-time spacecraft dynamics simulator , 1997, 16th DASC. AIAA/IEEE Digital Avionics Systems Conference. Reflections to the Future. Proceedings.

[26]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[27]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[28]  J. Shirley,et al.  Europa’s ridged plains and smooth low albedo plains: Distinctive compositions and compositional gradients at the leading side–trailing side boundary , 2010 .