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Embedded Context Aware Diagnosis for a UAV SoC platform

Sara Zermani 1 Catherine Dezan 1 Chabha Hireche 1 Reinhardt Euler 2 Jean-Philippe Diguet 3
1 Lab-STICC_UBO_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance, UBO - Université de Brest
2 Lab-STICC_UBO_CID_DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Autonomous Unmanned Aerial Vehicles (UAVs) operate under uncertain environmental conditions and can have to face unexpected obstacles, weather changes and sensor or hardware/software component failures. In such situations, the UAV must be able to detect and locate the failure and to take adequate recovery actions. In this paper, we focus on the Health Management of the system depending on the context of the mission. The task of this Health Management is to monitor the status of the system components based on observations from sensors and appearance contexts, and it is designed by means of Bayesian Networks arising from the Failure Mode and Effects Analysis. We jointly introduce a framework to generate embedded software and hardware implementations for online and real-time observations, which are demonstrated on a Hybrid CPU/FPGA Zynq platform.
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https://hal.univ-brest.fr/hal-01520122
Contributor : Catherine Dezan <>
Submitted on : Tuesday, May 9, 2017 - 7:09:10 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:35 PM

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  • HAL Id : hal-01520122, version 1

Citation

Sara Zermani, Catherine Dezan, Chabha Hireche, Reinhardt Euler, Jean-Philippe Diguet. Embedded Context Aware Diagnosis for a UAV SoC platform. Microprocessors and Microsystems: Embedded Hardware Design (MICPRO), Elsevier, 2017, 51, pp.185-197. ⟨hal-01520122⟩

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