Modern systems frequently consist of a complex mixture of hardware and software. Model-based diagnosis typically assumes that the effects of the software can be summarised by the commands sent to the hardware and thus the software can be left out of the model. In our effort to build a diagnosis system for an autonomous underwater vehicle (AUV) we have an example where this is not the case—commands sent to the hardware are not all available to the diagnosis system for a variety of reasons. In addition, the software controlling the AUV, the mission script is frequently completely changed from one mission to the next. Taking advantage of the fact that the mission script has a relatively simple structure that does not include loops we show that a diagnosis model of the mission script can be generated automatically that integrates with the model of the physical hardware. We show that this model allows us to diagnose faults that cannot be detected from the hardware model alone.
How to Cite
Model-based diagnosis, consistency-based diagnosis, AUV
(Hayden et al., 2004a) Sandra C. Hayden, Adam J. Sweet, Scott E. Christa, Daniel Tran, and Seth Shul- man. Advanced diagnostic system on Earth Observing One, 2004. AIAA paper 2004-6108. AIAA Space Conference and Exhibit, 28-30 Sep., San Diego, CA, United States, 14pp.
(Hayden et al., 2004b) Sandra C. Hayden, Adam J. Sweet, and Seth Shulman. Lessons learned in the Livingstone 2 on Earth Observing One flight experiment. In Proc. AIAA 1st Intelligent Systems Tech. Conf., Am. Inst. Aeronautics and Astronau- tics, pages 1–15, 2004.
(Kurien and Nayak, 2000) James Kurien and P. Pan- durang Nayak. Back to the future for consistency- based trajectory tracking. In Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, pages 370–377. AAAI Press, 2000.
(Mateis et al., 2000) Cristinel Mateis, Markus Stumptner, and Franz Wotawa. Modeling Java programs for diagnosis. In Werner Horn, editor, ECAI, pages 171–175. IOS Press, 2000.
(McPhail, 2009) Stephen McPhail. Autosub6000: A deep diving long range AUV. Journal of Bionic Engineering, 6(1):55 – 62, 2009.
(Mikaelian et al., 2005) Tsoline Mikaelian, Brian C. Williams, and Martin Sachenbacher. Model-based monitoring and diagnosis of systems with software- extended behavior. In AAAI’05: Proceedings of the 20th National Conference on Artificial Intelligence, pages 327–333. AAAI Press, 2005.
(Pebody, 2007) Miles Pebody. The contribution of scripted command sequences and low level control behaviours to autonomous underwater vehicle control systems and their impact on reliability and mission success. OCEANS 2007 - Europe, pages 1–5, June 2007.
(Strutt, 2006) J.E. Strutt. Report of the inquiry into the loss of Autosub2 under the Fimbulisen, 2006. National Oceanography Centre Southampton Research and Consultancy Report, 12, 39pp.
(Veanes et al., 2009) Margus Veanes, Nikolai Bjørner, Yuri Gurevich, and Wolfram Schulte. Symbolic bounded model checking of abstract state machines. International Journal of Software Informatics, 3(2-3):149–170, 2009.
(Williams and Nayak, 1996) Brian C. Williams and P. Pandurang Nayak. A model-based approach to reactive self-configuring systems. In AAAI/IAAI-96, Vol. 2, pages 971–978, 1996.
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