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Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS
A project funded by NARI via the ARMD Seedling Fund

Our run-time monitoring of LTL safety specifications with Bayesian reasoning can enable a fire-fighting Unmanned Aerial System to fly!


Team Members
  • PI: Kristin Y. Rozier, civil servant, NASA Ames Research Center, Moffett Field, CA 94035, USA
  • Co-I: Johann Schumann, SGT, Inc., NASA Ames Research Center, Moffett Field, CA 94035, USA
  • Co-I: Corey Ippolito, civil servant, NASA Ames Research Center, Moffett Field, CA 94035, USA

Abstract

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We are designing a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certification of flight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardware-enabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We are pursuing a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.

Collaborators
  • Johannes Geist, M.S. research intern, University of Applied Sciences Technikum Wien, Vienna, Austria
  • Chetan S. Kulkarni, Research Engineer, SGT, Inc., NASA Ames Research Center, Moffett Field, CA
  • Eddy Mazmanian, civil servant, NASA Ames Research Center, Moffett Field, CA 94035, USA
  • Patrick Moosbrugger, M.S. research intern, University of Applied Sciences Technikum Wien, Vienna, Austria
  • Quoc-Sang Phan, Ph.D. research intern, Queen Mary University of London, UK
  • Thomas Reinbacher, Ph.D. research intern, Vienna University of Technology, Vienna, Austria
  • Indranil Roychoudhury, Computer Scientist, SGT, Inc., NASA Ames Research Center, Moffett Field, CA
  • Iyal Suresh, sophomore undergraduate intern, University of California, Los Angeles

The project team worked closely with members of the airborne science group at NASA Ames Research Center in Code SG. We would like to give special thanks to Matt Fladeland (ARC/SG), Ric Kolyer (ARC/SG), Bruce Storms (ARC/AOX), and the rest of the Dragon Eye team who we collaborated with for modification and flight testing of the NASA Dragon Eye aircraft systems.

Publications

Seminars
  • PI is Featured Speaker: K.Y. Rozier: "No More Helicopter Parenting: Intelligent Autonomous Unmanned Aerial Systems." NASA Ames' premier seminar series, the Director's Colloquium, special edition in honor of NASA Ames' 75th Anniversary celebration, by special invitation of the Office of the Chief Scientist. NASA Ames Research Center, Moffett Field, California, June 10, 2014. [Now available on the NASA Ames YouTube Channel under "Dr. Kristin Yvonne Rozier - No More Helicopter Parenting: Intelligent, Autonomous UAS" ]
  • Co-I gives Invited Talk: J. Schumann: ``Towards on-board, hardware-supported Sensor and Software Health Management for UAS.'' FORTISS, Technische Universitat Munchen, Germany, June 2014.