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Implémentation sur SoC des réseaux Bayésiens pour l'état de santé et la décision dans le cadre de missions de véhicules autonomes

Abstract : Autonomous vehicles, such as drones, are used in different application areas to perform simple or complex missions. On one hand, they generally operate in uncertain environmental conditions, which can lead to disastrous consequences for humans and the environment. Therefore, it is necessary to continuously monitor the health of the system in order to detect and locate failures and to be able to make the decision in real time. This decision must maximize the ability to meet the mission objectives while maintaining the security requirements. On the other hand, they are required to perform tasks with large computation demands and performance requirements. Therefore, it is necessary to think of dedicated hardware accelerators to unload the processor and to meet the requirements of a computational speed-up.This is what we tried to demonstrate in this dual objective thesis. The first objective is to define a model for the health management and decision making. To this end, we used Bayesian networks, which are efficient probabilistic graphical models for diagnosis and decision-making under uncertainty. We propose a generic model based on an FMEA (Failure Modes and Effects Analysis). This analysis takes into account the different observations on the monitors and the appearance contexts. The second objective is the design and realization of hardware accelerators for Bayesian networks in general and more particularly for our models of health management and decision-making. Having no tool for the embedded implementation of computation by Bayesian networks, we propose a software workbench covering graphical or textual Bayesian networks up to the generation of the bitstream ready for the software or hardware implementation on FPGA. Finally, we test and validate our implementations on the Xilinx ZedBoard, incorporating an ARM Cortex-A9 processor and an FPGA.
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Submitted on : Friday, April 6, 2018 - 10:36:16 AM
Last modification on : Monday, October 19, 2020 - 10:55:29 AM


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  • HAL Id : tel-01760253, version 1


Sara Zermani. Implémentation sur SoC des réseaux Bayésiens pour l'état de santé et la décision dans le cadre de missions de véhicules autonomes. Autre [cs.OH]. Université de Bretagne occidentale - Brest, 2017. Français. ⟨NNT : 2017BRES0101⟩. ⟨tel-01760253⟩



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