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Theses

UAV mission planning based on Prognosis & Health Management

Abstract : Rotorcraft Unmanned Aerial Vehicles (UAV) with take-off and vertical landing capabilities or multirotors have proved to be an efficient and low-cost solution for civil flight applications due to significant advances in the development of robust and more efficient altitude and attitude control strategies, planning and re-planning algorithms capable of detecting and avoiding obstacles and Fault Diagnosis & Fault Tolerant Control methods. In most applications where multirotors are used, they develop different task as exploration, photogrammetry, filming, mapping and more recently all those dedicated to precision agriculture such as irrigation and crop monitoring. During the task development, the multirotor executes a mission which consists to fly through a set of paths connected by n reference points (named way-points) inside a known or unknown area. However, during the mission development, different negative factors decrease the multirotor flight performance such as environmental conditions, occurrence of faults or failures in actuators/sensors and energetic limitations due to the power source constraints. The energetic limitation problem in a multirotor are due to power capabilities that on-board battery can supply. Due to power and energy requirements, multirotors are powered by Lithium Polymer batteries which are rechargeable batteries of Lithium-Ion technology. They possess a polymer electrolyte instead of a liquid electrolyte and provide high power and energy densities. However, according to the use due to the number of charge/discharge cycles and other factors like damage provoked by over-discharges, the battery performances tend to decrease. Such decrease or aging causes a reduction in the efficiency of the UAV multirotor flight by the decrease of the total mission time or flight endurance, and leads to maneuverability problem, which increases the risk of crash and collision. This thesis topic addresses the issues concerning to battery performances and its influence into the mission and path planning tasks. By considering model-based prognosis techniques and path planning methods, a hierarchy mission planning strategy based on energy consumption is proposed and validated at simulation level considering different flight situations. The UAV performances, as well as its capability to execute and fulfill a mission is weighted by the computation of the battery State of Health (SoH) which is an index to measure the degradation level of the battery. The SoH helps to estimate the battery Remaining Useful Life (RUL) and establishes the energy limitation by the computation of the Maximum Flight Endurance (MFE). Such information is necessary to path planning generation which not only consider the constraints related to the power source but also the scopes and limitations of the mission to be executed. In addition, the main concern of this thesis are long time-distance missions e.g exploration or inspection of remote areas where it is fundamental to have a proper use of energy aboard the multirotor.
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https://hal.univ-lorraine.fr/tel-02510381
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Ricardo Schacht Rodriguez. UAV mission planning based on Prognosis & Health Management. Automatic. Université de Lorraine; Centro Nacional de Investigación y Desarrollo Tecnológico (Cuernavaca, Mor., México), 2019. English. ⟨NNT : 2019LORR0257⟩. ⟨tel-02510381⟩

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