基于改进河马算法的农业无人机路径规划

Agricultural UAV Path Planning Based on Improved Hippopotamus Optimization Algorithm

摘要:

针对传统农用车辆的运输方式存在效率低、成本高以及安全性差的问题，提出了一种用于农业无人机路径规划的改进河马算法(Dynamic modified hippopotamus optimization, DMHO)。该算法综合了Lévy飞行、成长比例机制、自适应学习率的棱镜对立学习算法及随机扩散的优势，提升算法的全局搜索及探索能力。算法在23个经典基准函数的测试结果表明，与原始河马算法等8种算法相比，DMHO在21个函数上展现出最优性能。构建丘陵种植区域无人机飞行环境的三维地形，搭建农业无人机在此环境下的路径规划模型，设计满足多条件约束的代价函数。在3种不同复杂程度的飞行任务中，DMHO找寻的平均适应度最短,相较于原始河马算法标准差分别降低33.39%、72.81%和7.08%，表现出显著的优越性和稳定性。

Abstract:

Aiming to address the inefficiency, high cost, and poor safety of traditional agricultural vehicle-based transport, a dynamic modified hippopotamus optimization (DMHO) was proposed for agricultural UAV path planning. The algorithm synthesized the advantages of Lévy flight, growth ratio mechanism, lens opposition-based learning (LOBL) algorithm with adaptive learning rate and stochastic diffusion to comprehensively improve the algorithm’s global search and exploration capabilities. Based on the test results of the algorithm on 23 classical benchmark functions, it was shown that dynamic modified hippopotamus optimization exhibited optimal performance on 21 of these functions and had the best optimization searching effect compared with eight algorithms such as the original hippopotamus optimization algorithm. The three-dimensional terrain of the unmanned aerial vehicle flight environment in the hilly planting area was constructed, the trajectory planning model of the agricultural unmanned aerial vehicle in this environment was built, and the trajectory planning cost function was designed to satisfy the multi-conditional constraints. In the three different complexity tasks, dynamic modified hippopotamus optimization had the lowest average fitness result among all the compared algorithms, and the standard deviation in the test results was decreased by 33.39%, 72.81% and 7.08%, respectively, in comparison with hippopotamus optimization algorithm. The dynamic modified hippopotamus optimization algorithm demonstrated remarkable superiority and stability in experimental evaluations.