Unmanned Aircraft Systems (UAS) with autonomous decision-making capabilities are of increasing interest for a wide area of applications such as logistics and disaster recovery. In order to ensure the correct behavior of the system and to recognize hazardous situations or system faults, we applied stream runtime monitoring techniques within the DLR ARTIS (Autonomous Research Testbed for Intelligent System) family of unmanned aircraft. We present our experience from specification elicitation, instrumentation, offline log-file analysis, and online monitoring on the flight computer on a test rig. The debugging and health management support through stream runtime monitoring techniques have proven highly beneficial for system design and development. At the same time, the project has identified usability improvements to the specification language, and has influenced the design of the language.
CITATION STYLE
Adolf, F. M., Faymonville, P., Finkbeiner, B., Schirmer, S., & Torens, C. (2017). Stream runtime monitoring on UAS. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10548 LNCS, pp. 33–49). Springer Verlag. https://doi.org/10.1007/978-3-319-67531-2_3
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