Optimizing Energy Consumption Of Rotor Unmanned Aerial Vehicle Based On Path Planning

Due to its unique high-altitude advantages,rotor Unmanned Aerial Vehicles(UAVs)have attracted more and more attention,and people are eager to use UAVs to perform some high-risk tasks and save costs by using their advantages in high-altitude.Although the application of rotor UAV has a lot of room for development,its limited endurance limits its development.Therefore,it is crucial to optimize the energy consumption of UAVs when they perform their tasks.In order to achieve this goal,a reliable and accurate energy consumption measurement model is required to measure the energy consumption.This thesis proposes a rotor UAV energy consumption measurement model with multiple flight futures.Based on this model,combined with the path planning method,the flight path of the UAV in the mission scene is optimized to achieve the purpose of reducing energy consumption.This thesis also proposes an improved genetic algorithm to solve the energy consumption optimization problem of rotor UAV.The main contents and conclusions of this thesis are as follows:1)Aiming at the optimization of the flight energy consumption of the rotor UAV,an accurate measurement model of the flight energy consumption of the rotor UAV is proposed.The measurement of flight energy consumption is the basis of energy consumption optimization.However,the existing energy consumption models either only select a small number of flight features to establish the energy consumption measurement model,or consider that each feature is independent of each other,which makes the measurement effect of these energy consumption models is not ideal.To this end,this thesis selects the most comprehensive flight features and considers the coupling between the features to estimate the flight energy consumption.The effectiveness of the energy consumption measurement model proposed in this thesis is proved by simulation experiments.2)A path planning model for optimal energy consumption of rotor UAV with multiflight characteristics is proposed.Combined with the energy consumption measurement model proposed in 1),the path planning problem model is formulated as a traveling salesman problem.The goal is to make the flight path consumed by the rotor UAV when it completes traversing all mission points and returns to the starting point.An improved genetic algorithm is used to solve the optimization problem,and the model of the problem is solved in the MATLAB simulation environment.Simulation results show that a flight path with multi-flight features produces less flight energy than a flight path with few or single feature.3)An opposition-based learning flight path genetic algorithm based on is proposed.Using the opposition-based learning method initialize population,and an optimal retention crossover operator is designed in combination with the structure of the individuals generated by the problem model.Simulation experiments are carried out with MATLAB,and the experimental results show that the algorithm proposed in this thesis reduces the calculation time,improves the convergence speed of population and optimizes the local search ability.