ARMD Seedling ARMD Seedling
Links
NARI
ARMD Seedling
TACAS14
RV14
Director's Colloquium
NASA TM Final Report

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

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, University of Applied Sciences Technikum Wien, Vienna, Austria
  • Eddy Mazmanian, civil servant, NASA Ames Research Center, Moffett Field, CA 94035, USA
  • Patrick Moosbrugger, University of Applied Sciences Technikum Wien, Vienna, Austria