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In this work, a Model-based Prognosis algorithm to predict the Flight Endurance (FE) and the Remaining Mission Time (RMT) for a class of multirotor UAVs powered by Lithium Polymer (Li-Po) batteries is presented. A Safety Voltage Threshold (SVT) in the State of Charge (SoC) of the battery is established before reaching of End of Discharge (EoD), and the SVT is defined taking into account the existing relationship between the voltage and the SoC of the battery. The FE is predicted by forecasting when the SVT is reached and the RMT is computed as the difference between the FE and the actual time. The prediction is developed in three sequential steps: 1) estimation of the battery SoC, 2) propagation and prediction of the estimated SoC in the future to reach the SVT, and 3) updating the prediction. The Model-based Prognosis algorithm is based on the mathematical model of the UAV propulsion system, which is made up by a set of BrushLess DC motors and a Li-Po battery. The effectiveness of the proposed algorithm was tested in simulation and the results obtained demonstrate the efficacy of the proposed method to accurately predict the FE and RMT during the development of a mission.

International audience; In this paper, the impact of fault effects occurring in actuators on energy consumption for multirotor UAV during mission development is analyzed. The multirotors are typically powered by Lithium Polymer batteries where the total mission time depends on the energy available on board. According to battery and actuators health, the discharge rate tends to increase which decrease the flight endurance causing that battery to discharge completely without guaranteeing the fulfillment of the mission or even a safety landing. In that sense, a model able to determines the maximum energy and flight endurance is used considering the battery discharge, State of Charge (SoC) and State of Health (SoH) and its impact during the mission execution is evaluated considering the fault effects in actuators modeled as loss of effectiveness. The proposed approach is tested at simulation level considering an hexarotor UAV.

International audience; The multirotor UAVs with Vertical Take-off and Landing (VTOL) capabilities are powered by Lithium Polymer (Li-Po) batteries which supply the energy to launch either indoor and outdoor missions. However, according to number of charges/discharges the battery tends to exhibit an aging behavior. An aging battery could restrict the flight capabilities and cause maneuverability problems increasing the risks of collision. In that sense, in this paper a path planning strategy that take into account the State of Health (SoH) of the Li-Po battery for a class of multirotor UAVs is introduced as an optimization problem. The SoH is computed by introduce a degradation model which affects the battery discharge performance, and an optimization problem is defined to find the feasible path between way-points while minimize the energy supplied by the battery according to the present SoH. The path planning strategy is applied to an hexarotor UAV, and the simulation results show the feasibility of the proposed methodology.

International audience; Nowadays, it exists path planning strategies dedicated to generate trajectories considering different navigation issues in UAV multirotors, such as 3D navigation in cluttered and uncluttered environments, obstacle avoidance, and path re-planning. Such path generators are mainly based on the dynamics associated to position and orientation of the UAV, and the attenuation of external disturbances as the wind. However, one of the main limitations of these methods is that they do not take into account the relationship between the path planning task and the energy consumption associated with the battery performance or State of Health (SoH). In this work, a path planning generation algorithm that take into account the evolution of the battery performance is presented. First, the computation of the battery SoH is realized by introducing two degradation models. Subsequently, the path planning algorithm is defined as a multi-objective optimization problem where the objective is to find a feasible trajectory between way-points whiles minimizing the energy consumed and the mission final time depending on the variation of the battery SoH. Finally, the proposed path planning algorithm is compared with a classical path generation method based on polynomial functions to evaluate the minimization of the energy consumption. The simulation results demonstrate that the proposed path planning algorithm is able to generate feasible and minimum energy trajectories despite the constraints in the battery SoH.

International audience; Measuring or estimating adequately and accurately the state of charge (SoC) of a Li-Po battery, which powered an UAV during its flight allows to know the limits of the mission that the UAV is developing and to maximize the energy supplied by the battery. In this sense this paper presents a methodology to estimate the state of charge (SoC) of a Li-Po battery through an Extended Kalman Filter (EKF) during the flight of an UAV. By considering the mathematical model of the propulsion system of an UAV hexacopter, the power consumption is computed, and the SoC of the battery is estimated. Finally, a strategy based on estimated SoC is presented to predict the End-of-Discharge (EoD) during the development of a mission of and UAV hexacopter. The simulation results show the effectiveness of the proposed method.