优化农田耕地路径基于混合适应性邻里搜索算法
Han Lv1,2,3, Zhixin Yao1,2,3, Taihong Zhang2,3
1College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了对自主农业运营的层次适应性邻里搜索 (HANS) 框架,优化了大型田地的路径规划. 与现有方法相比,HANS提高了覆盖率并降低了燃料消耗.
科学领域:
- 农业工程 农业工程
- 机器人技术 机器人技术 机器人技术
- 优化算法 优化算法
背景情况:
- 大规模农业的路径规划是复杂的,因为不规则的田地形状和边界不确定性.
- 现有的方法很难有效地平衡路径效率,能源消耗和覆盖质量.
- 自主运行需要强大而适应性的路径规划解决方案.
研究的目的:
- 开发一个新的优化框架,HANS,用于自主农业路径规划.
- 为了应对不规则的场地几何形状,边界噪声和多目标优化的挑战.
- 为了提高路径效率,节能,并在大规模农业覆盖质量.
主要方法:
- 引入了基于主要组件分析 (PCA) 的主要轴提取方法,以进行可靠的边界分析.
- 集成适应性大邻里搜索 (ALNS) 用于全球搜索和tabu搜索 (TS) 用于本地优化.
- 采用基于帕雷托的多目标决策支持策略,考虑动力学,转和能源成本.
主要成果:
- 与固定方向规划相比,HANS的平均覆盖率提高了0.51%,燃料消耗降低了4.34%.
- 与GA,PSO,TS和SA相比,HANS的路径长度缩短了0.37-0.83%,覆盖率提高了0.34-1.11%,能源减少了0.61-1.03%.
- 该框架通过反学习机制展示了具有竞争力的计算成本和适应性.
结论:
- 汉斯 (HANS) 框架有效地解决了大型自主农业运营中的路径规划挑战.
- 汉斯为优化覆盖范围,能源和路径长度提供了一种实用和高效的解决方案.
- 战略意识的层次化混合方法为农业机器人提供了强大的和可适应的方法.
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