Onboard Autonomy on the Intelligent Payload Experiment CubeSat Mission

The Intelligent Payload Experiment (IPEX) is a CubeSat that flew from December 2013 through January 2015 and validated autonomous operations for onboard instrument processing and product generation for the Intelligent Payload Module of the Hyperspectral Infrared Imager (HyspIRI) mission concept. IPEX used several artificial intelligence technologies. First, IPEX used machine learning and computer vision in its onboard processing. IPEX used machine-learned random decision forests to classify images onboard (to downlink classification maps) and computer vision visual salience software to extract interesting regions for downlink in acquired imagery. Second, IPEX flew the Continuous Activity Scheduler Planner Execution and Re-planner AI planner/scheduler onboard to enable IPEX operations to replan to best use spacecraft resources such as file storage, CPU, power, and downlink bandwidth. First, the ground and flight operations concept for proposed HyspIRI IPM operations is described, followed by a description ...

[1]  Alex S. Fukunaga,et al.  Automatic detection of dust devils and clouds on Mars , 2008, Machine Vision and Applications.

[2]  Steve Chien,et al.  Flood detection and monitoring with the Autonomous Sciencecraft Experiment onboard EO-1 , 2006 .

[3]  Kiri Wagstaff,et al.  Dynamic Landmarking for Surface Feature Identification and Change Detection , 2012, TIST.

[4]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[5]  Rob Sherwood,et al.  Using Iterative Repair to Improve the Responsiveness of Planning and Scheduling , 2000, AIPS.

[6]  David R. Thompson,et al.  Probabilistic surface classification for rover instrument targeting , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  David R. Thompson,et al.  Real‐Time Orbital Image Analysis Using Decision Forests, with a Deployment Onboard the IPEX Spacecraft , 2016, J. Field Robotics.

[8]  A. Harris,et al.  Automated volcanic eruption detection using MODIS , 2001 .

[9]  Steve A. Chien,et al.  Onboard Science Processing Concepts for the HyspIRI Mission , 2009, IEEE Intelligent Systems.

[10]  Benjamin Cichy,et al.  Autonomous detection of cryospheric change with hyperion on-board Earth Observing-1 , 2006 .

[11]  David R. Thompson,et al.  Autonomous Spectral Discovery and Mapping Onboard the EO-1 Spacecraft , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  Roberto Cipolla,et al.  Semantic texton forests for image categorization and segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Rob Sherwood,et al.  Using Autonomy Flight Software to Improve Science Return on Earth Observing One , 2005, J. Aerosp. Comput. Inf. Commun..

[15]  Russell Knight,et al.  Planning Coverage Campaigns for Mission Design and Analysis: Clasp for the Proposed DESDynI Mission , 2012 .

[16]  D. Roberts,et al.  Sensitivity of Narrow-Band and Broad-Band Indices for Assessing Nitrogen Availability and Water Stress in an Annual Crop , 2008 .

[17]  D. Roy,et al.  The MODIS fire products , 2002 .

[18]  A. Harris,et al.  MODVOLC: near-real-time thermal monitoring of global volcanism , 2004 .

[19]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[20]  Nathalie A. Cabrol,et al.  Smart, texture‐sensitive instrument classification for in situ rock and layer analysis , 2013 .

[21]  Laurent Itti,et al.  Interesting objects are visually salient. , 2008, Journal of vision.

[22]  P. Pandurang Nayak,et al.  Remote Agent: To Boldly Go Where No AI System Has Gone Before , 1998, Artif. Intell..

[23]  E. Anderson,et al.  MODIS-BASED FLOOD DETECTION, MAPPING AND MEASUREMENT: THE POTENTIAL FOR OPERATIONAL HYDROLOGICAL APPLICATIONS , 2006 .

[24]  Tara A. Estlin,et al.  AEGIS Automated Science Targeting for the MER Opportunity Rover , 2012, TIST.

[25]  Steve Ankuo Chien,et al.  Monitoring active volcanism with the Autonomous Sciencecraft Experiment on EO-1 , 2006 .

[26]  Yang Liu,et al.  Automating X-ray Fluorescence Analysis for Rapid Astrobiology Surveys. , 2015, Astrobiology.

[27]  John R. Townshend,et al.  A new global raster water mask at 250 m resolution , 2009, Int. J. Digit. Earth.