基于改进海鸥算法的无人机山地果园三维路径规划方法

Three Dimensional Path Planning Method for UAV in Mountainous Orchards Based on Improved Seagull Optimization Algorithm

摘要:

针对山地果园复杂地形下植保无人机多目标航点作业效率低、能耗高的难题，提出一种基于改进海鸥优化算法（PSOA）的三维路径规划方法。首先，基于果树冠层顶点坐标建立三维果园模型；其次，建立多旋翼无人机能量损耗模型以评估和量化路径性能；最后，针对海鸥优化算法 (SOA) 容易陷入局部最优、收敛速度慢等问题，引入Lévy飞行机制、自适应控制因子及精英保留策略进行多维度改进。在自建的果园三维模型上的实验验证表明： PSOA 在收敛精度、收敛速度和稳定性方面显著优于主流优化算法。仿真结果显示，与传统SOA相比， PSOA算法使路径总长度减少48.74%，总转角减少24.36%，预期能耗降低49.10%，预计作业时长缩短33.3%，路径性能优化效果显著。该研究为山地果园植保无人机提供了高效、低耗的三维路径规划解决方案，显著提升了复杂环境下的作业效率与经济性。

Abstract:

It is crucial to achieve safe and efficient multi-target waypoint operation of crop protection drones in complex environments of mountainous orchards, reduce energy consumption of drone operations, and implement reasonable path planning. Addressing the issues of low efficiency, convoluted search paths, and slow convergence speed in traditional seagull optimization algorithm (SOA). A 3D path planning method was designed based on the improved seagull optimization algorithm (PSOA). Firstly, based on the actual scene of mountainous orchards, the vertex coordinates of the fruit tree canopy were obtained and a three-dimensional orchard model was established. Then, an energy loss model for rotary wing unmanned aerial vehicles was established in the three-dimensional path planning of a single work area to evaluate and optimize flight path performance, in order to achieve the most energy-efficient three-dimensional path planning. Finally, in response to the problem of seagull optimization algorithm easily getting stuck in local optima and slow convergence speed, the Lévy flight mechanism was introduced to expand the search range, the adaptive control factor was used to improve the search ability, and the elite retention strategy was applied to maintain population diversity, in order to obtain the globally optimal three-dimensional flight path. Comparative verification of benchmark test functions showed that PSO outperformed other mainstream optimization algorithms in terms of convergence accuracy, convergence speed, and stability. The simulation results of the experimental field showed that compared with traditional SOA, the PSO algorithm reduced the total path length by 48.74%, the total turning angle by 24.36%, the expected energy consumption by 49.10%, the expected operation time by 33.3%, and significantly reduceds the number of dangerous nodes. This method can optimize the operation path based on the crown vertex position of fruit trees and the energy consumption characteristics of drones, providing an effective solution for the three-dimensional path planning problem of crop protection drones in mountainous orchards.