Research And Experiment On Scheduling Planning Method Of UAV Multi-field Spraying Route In Hilly Areas

Hilly and mountainous areas have complex environments and weak infrastructure,which make it difficult to achieve precise and effective plant protection operations.Traditional aerial operation route planning methods rely too much on subjective experience,often leading to long routes,low plant protection efficiency,and difficulty in effectively and accurately carrying out aerial plant protection operations.This thesis takes the multi-field operation environment as the research object,aiming to solve the problems of inaccurate coverage range,long routes,etc.in plant protection operations in hilly and mountainous areas.A drone plant protection scheduling and planning algorithm is proposed,which optimizes the drone operation path planning by optimizing the regional full coverage route planning method,the crossover and mutation operators of the genetic algorithm,and proposes a serial fusion scheduling route planning algorithm combining adaptive dynamic genetic algorithm and ant colony bisection iterative algorithm for drone plant protection route planning in multi-field hilly and mountainous areas.Through simulation comparison with standard heuristic algorithms,the optimization performance and convergence characteristics of the algorithm are explored.The main work includes the following aspects:(1)The establishment of the projection coordinate system of the operation area environment is studied.By applying geographic information system(GIS)software to process elevation data using Mercator projection method,a plane coordinate system of single and multiple blocks is established,which provides a two-dimensional mathematical calculation environment for route planning method research.(2)A drone plant protection route scheduling and planning algorithm is studied.For different operation environments,based on the regional full coverage route planning method,the crossover and mutation operators of the genetic algorithm are optimized,and a serial fusion scheduling route planning algorithm combining adaptive dynamic genetic algorithm and ant colony bisection iterative algorithm is proposed for drone plant protection route planning in multi-field hilly and mountainous areas.Through simulation comparison with standard heuristic algorithms,the optimization performance and convergence characteristics of the algorithm are explored.(3)A drone plant protection route scheduling and planning algorithm based on plant protection zoning is studied.Considering factors such as single-flight plant protection capacity and flight allocation,drone operation replenishment points are planned in small and scattered terrain in hilly and mountainous areas.With the minimization of plant protection cycle as the optimization objective,a path optimal planning scheme based on partition clustering fusion algorithm is summarized,and the feasibility and convergence performance of the algorithm are explored through simulation experiments.(4)In order to verify the theoretical feasibility of the proposed clustering fusion planning method,an algorithm simulation model is established in Matlab,and simulation is carried out for the proposed method.By importing the planning results of various methods into the drone flight control ground station,field tests are carried out to verify the effectiveness and reliability of this route planning method in practical applications.The test results show that compared with traditional methods,the proposed method can reduce flight cost,reduce redundant coverage rate and turn times under the premise of ensuring full coverage of plant protection,thereby improving operation efficiency.