Research On Motion Planning Method Of Lifting UAV

With the development of UAV technology,the UAV external hanging load flight(referred to as UAV lifting)technology has been widely used in aerial photography,transportation,disaster relief and other fields,and UAV motion planning has gradually become a hot research topic.Traditional path planning algorithms face problems such as poor environmental adaptability,easy entry into a dead cycle,low search efficiency,and insufficient smoothness in the generated path when facing complex obstacle environments of unmanned aerial vehicles.This topic is based on the traditional path planning algorithm,aiming at the problem of poor adaptability to the environment,establishes the environment complexity model,proposes an improved path planning algorithm,and applies it to the lifting and motion planning of UAVs to achieve the smooth flight of lifting UAVs.This project mainly includes the following research work:(1)Establishment of an optimal step size and environmental complexity model based on path planning algorithms.This article is based on the traditional path planning algorithm model and analyzes and compares the characteristics of path planning algorithms such as A-star algorithm,fast search random tree(RRT)algorithm,and artificial potential field method.It summarizes and summarizes the main problems of existing path planning algorithms.And in response to the problem of poor environmental adaptability,the performance parameters of path planning are optimized.Genetic algorithm is used to find the relationship between the optimal step size and environmental complexity,establish a model and expression between the two,and form a three-dimensional spatial optimal step size and environmental complexity model for unmanned aerial vehicle path planning.(2)The proposal of a variable step RRT path planning algorithm based on environmental complexity.Traditional path planning algorithms cannot dynamically change path planning parameters based on the real-time environment around them.Due to the varying complexity of local obstacles during the path finding process,fixed step path planning algorithms have low efficiency and poor stability.This article proposes a variable step RRT path planning algorithm based on sliding windows,based on the optimal step size and environmental complexity model in three-dimensional space,to address this issue.This algorithm can dynamically select the optimal step size according to the local environment,and improve the overall performance of the UAV path planning algorithm.Finally,the algorithm was programmed and simulated using MATLAB to verify that the variable step path planning algorithm proposed in this paper has the advantages of efficiency,stability,and low cost compared to traditional algorithms.(3)Research on trajectory planning for lifting unmanned aerial vehicles.Path planning only involves the construction and connection of path points in space,without considering the size,attitude,and speed limitations of the lifting drone.This article studies its motion characteristics.Based on the establishment of a dynamic model for a lifting drone,path planning is transformed into trajectory planning based on parameters such as drone attitude,position,velocity,and acceleration.The relationship between "position time" and "velocity time" is presented,and simulation is conducted on a computer and field experiments are conducted.In summary,this article establishes an environmental complexity model to address the existing problems of path planning algorithms.Based on the path planning algorithm,its parameters are optimized and improved,and a variable step path planning algorithm is proposed,demonstrating its superiority.In addition,based on the motion characteristics of the lifting UAV,the controller is designed and motion planning is carried out,and then simulation experiments are carried out to verify its feasibility.