| 肖根福,刘欢,张祥明,郭猷敏.基于混合粒子群算法的农情监测无人机路径规划[J].井冈山大学自然版,2021,42(3):59-63 |
| 基于混合粒子群算法的农情监测无人机路径规划 |
| PATH PLANNING OF UAV FOR AGRICULTURAL MONITORING BASED ON HYBRID PARTICLE SWARM OPTIMIZATION |
| 投稿时间：2020-02-23 修订日期：2020-05-18 |
| DOI：10.3669/j.issn.1674-8085.2021.03.011 |
| 中文关键词: 粒子群算法 无人机 农情监测 路径规划 |
| 英文关键词: particle swarm optimization UAV agricultural monitoring path planning |
| 基金项目:国家自然科学基金项目（42061055）；江西省自然科学基金项目（20202BABL202047）；江西省高校人文项目（YS20129）；江西省教育厅科技计划项目（GJJ190548）；吉安市科技计划重点项目（吉财教指[2020]83号科技合作2） |
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| 中文摘要: |
| 用无人机对果树病虫害进行巡航拍摄是有较大潜力的农情监测方法，本文以飞行时间最短为目标，建立了农情监测无人机路径规划的数学模型。结合遗传算法、模拟退火算法的思想，在粒子群算法中引入交叉、变异、替换操作，提出了一种混合粒子群算法来求解无人机路径规划的数学模型。实验表明，无人机路径规划数学模型可以被混合粒子群算法有效求解，且混合粒子群算法的性能优于遗传算法和模拟退火算法。 |
| 英文摘要: |
| It is a potential method of agricultural monitoring using UAV to shoot fruit diseases and insect pests. In this paper, aiming at the shortest flight time, the mathematical model of UAV path planning for agricultural monitoring is established. Combined with the idea of genetic algorithm and simulated annealing algorithm, a hybrid particle swarm optimization algorithm is proposed to solve the UAV path planning mathematical model by introducing crossover, mutation and replacement operations into the standard particle swarm optimization algorithm. Experiments show that the mathematical model of UAV path planning can be effectively solved by the hybrid particle swarm optimization, and the performance of the hybrid particle swarm optimization is better than that of genetic algorithm and simulated annealing algorithm. |
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