Research And Implementation Of Drone Spray Planning Combination Algorithm Based On Energy Optimization

In recent years,with the rapid development of UAV technology,UAVs have played an important role in modern military warfare and have been applied more and more in civilian fields such as aerial photography,logistics,and agricultural spraying.As an important intelligent agricultural equipment,plant protection and drone integrates various technologies in agriculture,information technology,mechanical engineering,computer technology,communications technology,automation control and satellite navigation and positioning.With the Ministry of Agriculture officially launching the “losing weight and reducing drugs” action,plant protection drones will become the main force of the state plant protection services.One of the main tasks of plant protection drones is to prevent or control pests and diseases by low-altitude spraying,and spraying planning is an important part of the drone application system.The operational quality and operating efficiency of drones are directly affected by the planning effect.At present,there are few related studies on the drone spraying planning method,and some of its planning methods need to be optimized.Therefore,this paper conducts an in-depth study on the drone spraying planning method and focuses on the low degree of autonomy and efficiency of drone spraying operations.Problems such as low,inefficient energy loss,and complex spray tasks are proposed,and a spray planning and composition algorithm is proposed.The combined algorithm is used to plan the drone spraying operation so that the UAV operation energy consumption and load consumption can be optimized,which has theoretical and practical significance in the field of plant protection application.The spray planning combination algorithm studied in this paper includes route planning algorithm,payload algorithm and energy early warning algorithm.Based on the route presetting of the operation area,an accurate presetting algorithm for the full coverage route was proposed,effectively solving the problem of inaccurate presetting of the UAV operation route and the occurrence of heavy discharge and missing spray;The mathematical relationship between the operating voyage and the drone voyage under the maximum load of the drone results in the minimum number of drone operations,and the construction and solution of the relationship equation between the drone sub-flight voyage and the drone load is obtained.Human-machine operation return point and replenishment point,achieving optimal return route planning,reducing the non-spraying operation range of UAV,reducing the invalid energy consumption loss of UAV,and increasing the energy for the excess load of the UAV under the full load The problem of loss is determined by the route planning algorithm to determine the flight voyages of each drone operation,further determining the sub-optimal load of the UAV based on the spray flow rate,and proposing the optimal load algorithm for payload planning can further reduce the load consumption and energy loss;In view of the unmanned aerial vehicle's energy limitation and operator's lack of experience in operation,an energy warning algorithm is added to the route planning and load planning to calculate the operational energy consumption of the drone and the remaining energy of the battery in real time,and to estimate the drone The required energy for one train trip denied the drone's low-power starting operation,greatly improving the operational safety of the drone.Finally,on the basis of the plant protection drone route planning algorithm,combined with the payload utilization principle of the payload algorithm and the security planning mechanism of the energy early warning algorithm,a drone spraying planning method based on the combined optimization algorithm is proposed,so that The plant protection drone autonomously operates under the condition of safety and can greatly reduce the invalid energy loss during the operation,reduce the working time,and improve the work efficiency.On the basis of realizing the function of spray planning combined algorithm,the ground control station software suitable for the drone spraying system was written.Based on the Visual studio 2017 development environment,the C# programming language was developed under the framework of.net framework 4.6.Data communication between UAV and ground station is realized through serial communication technology,virtual dashboard is developed based on Open GL and GDI+ to display data,and map service provided by map provider is acquired based on GMap.NET map control and HTTP protocol.The terminal generates a real-time electronic navigation three-dimensional or two-dimensional map of the operational area where the drone is located,providing a visual environment for drone spraying planning.Through the secondary development of the GMap.NET map control,the UAV flight path planning function and the purpose of simplifying the planning operation are achieved.Based on the study of spray mission planning methods and ground station development,in the experiments of field trials,route planning algorithms and payload planning algorithms for unmanned aerial vehicle dredging operations,the operations planned through the algorithm are less expensive than the operations without algorithmic planning.In the return flight of 272.5m,the voyage saving rate was 23.7%,the saving load was 1000 ml,and the load saving rate was 16.7%.The test results verify the feasibility and effectiveness of the route planning algorithm and the payload algorithm in practical application.In the experiment of the energy early-warning planning algorithm,the operation task is 5 flights,and the remaining power before the fifth flight of the UAV is 281.49 m AH,and the estimated power required by the algorithm for the next operation is 621.10 m AH,effectively avoiding the takeoff operation of the drone with low power.The experimental results verify the effectiveness of the spray planning algorithm designed in this paper in reducing the energy consumption and drug consumption of UAVs and improving the operational safety.The research results of this paper will help promote the development of plant protection UAV safety,efficiency,and intelligence.