Research On Path Planning Method Of Plant Protection UAV Based On Meta-heuristic Algorith

As an emerging intelligent agricultural operation equipment,plant protection unmanned aerial vehicles(UAVs)is highly integrated with mechatronics,computer,information and communication,automatic control,geographic information positioning,and other related technologies.It is a hot research spot in the current development of modern agriculture.Path planning is an important guarantee to achieve high efficiency and safety of plant protection UAVs operation.The traditional path planning algorithms are difficult to solve the complex and multi-constrained combinatorial optimization problem of plant protection UAVs path planning at the present stage because of their high cost,slow convergence speed,easy fall into local optimum and insufficient smooth planning path.The borrowed metaheuristic algorithm has apparent advantages in path planning due to its concise mathematical representation and accurate object description capability.It is an effective means to solve complex and multi-constrained combinatorial optimization problems quickly and efficiently.Therefore,this paper focuses on the research of the path planning of plant protection UAVs based on the metaheuristic algorithm according to different agricultural operation environment maps,constraints and optimization objectives,and other problems to achieve the optimization of the working path of plant protection UAVs,which has important theoretical and practical significance in the application development of plant protection UAVs.The main research contents are described as follows:(1)Aiming at the problems of traditional neural network algorithms that rely on a priori knowledge and the low efficiency of handling unknown environmental information and global search,a full-coverage path planning algorithm based on the combination of neural network forward propagation and artificial bee colony algorithm is proposed.It realizes automatic generation and evaluation of full-coverage path during model training by constructing a comprehensive evaluation function.The simulation results show that the proposed method greatly improves the efficiency of UAV full-coverage plant protection operations.(2)For the problem that the traditional gazelle optimization algorithm has insufficient global search capability and is easily trapped in local optimum,a mathematical model of path planning under the coupling of multiple constraints,such as obstacles,UAV flight height,and turning angle,is constructed.Then,an improved gazelle optimization algorithm based on logistic-tent chaotic initialization and dynamic adaptive global search strategy is proposed,effectively improving the algorithm’s global search capability.The global search capability of the algorithm is effectively enhanced.The simulation results show that the proposed method can optimize plant protection UAV operation paths with different constraints.The proposed method’s feasibility and effectiveness for point-to-point threedimensional path planning of plant protection UAVs are verified by taking the static obstacle environment of mountain orchards as an example.(3)Based on the above research results,a prototype system of plant protection UAV path planning is designed and developed using Spring Boot and Spring Cloud technologies,providing functional modules,such as personnel and equipment information management based on role assignment,operation path planning,and real-time synchronization and control of operation information,to realize the precise application of crops,improve the efficiency of plant protection UAV operations,reduce labour costs,and provide more convenient and efficient services for agricultural production.