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Etude et implémentation sur SoC-FPGA d'une méthode probabiliste pour le contrôle de mission de véhicule autonome

Abstract : Autonomous systems embed different types of sensors, applications and powerful calculators. Thus, they are used in different fields of application and perform various simple or complex tasks. Generally, these missions are executed in nondeterministic environments with the presence of random events that can affect the mission's progress. Therefore, it is necessary to regularly assess the health of the system and its hardware and software components in order to detect failures using Bayesian Networks.Subsequently, a decision is made by the mission planner by generating a new mission plan that ensures the mission in response to the detected event. This decision is made using the Markov Decision Process model based on constraints such as the mission objective, the health status of sensors and embedded applications, the mission policy "safety policy" or "mission first policy", etc. As autonomous systems perform different tasks that require different performance, it is necessary to consider the use of hardware accelerators on SoC-FPGA in order to meet high-performance computing constraints and unload the CPU if needed.
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Submitted on : Thursday, March 19, 2020 - 5:37:09 PM
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Chabha Hireche. Etude et implémentation sur SoC-FPGA d'une méthode probabiliste pour le contrôle de mission de véhicule autonome. Systèmes embarqués. Université de Bretagne occidentale - Brest, 2019. Français. ⟨NNT : 2019BRES0067⟩. ⟨tel-02512555⟩

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