基于风的灵敏度,优化了基于多个无人机的超低体积喷雾
Dachuan Zheng1, Bing Wang2, Yuzhe Lin3
1Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, Shandong, China. zhengdachuan@stu.ouc.edu.cn.
Scientific reports
|March 11, 2026
概括
这项研究引入了对风敏感的无人机喷框架,用于城市公共卫生,优化杀虫剂的应用,以控制载体传播疾病. 改进的算法显著改善了覆盖范围,减少了能源使用,提供了更安全,低碳的解决方案.
科学领域:
- 机器人和自动化 机器人和自动化
- 环境科学 环境科学
- 公共卫生 公共卫生
背景情况:
- 城市公共卫生需要高效的空中操作来控制载体传播疾病.
- 现有的无人机农药应用方法在复杂的风条件下面临挑战,需要优化覆盖范围和安全性.
- 超低体积 (ULV) 喷需要精确的轨迹规划,以最大限度地提高效率并最大限度地减少对环境的影响.
研究的目的:
- 为城市环境中多无人机ULV喷开发一个风感应轨迹优化框架.
- 将风引发的伸缩沉积足迹 (WISDF) 模型与农药效率衰减整合到一个统一的优化目标中.
- 为了提高运营效率,覆盖范围,沉积均性和安全性,同时最大限度地减少过度喷雾和能源消耗.
主要方法:
- 为多无人机ULV喷开发一个风感应轨迹优化框架.
- 整合WISDF模型和农药有效性衰变成为一个统一的目标功能.
- 实施一个增强的协作灰狼优化 (C-GWO+) 算法与基于对立的学习,合作子群和停滞重置策略.
- 在复杂的风场中进行实验验证,将C-GWO+与PSO,SSA和Lawnmower基线进行比较.
主要成果:
- C-GWO+算法实现了卓越的解决方案质量,在全面适应性方面,PSO的表现优于5.7%,SSA的表现优于16.3%.
- 与Lawnmower基线相比,C-GWO+在保持沉积的一致性的情况下,减少了约43%的操作能源.
- 统计分析证实,与标准GWO相比,覆盖率,统一性和安全合规性 (过度喷涂控制) 显著改善 (p < 0.001).
结论:
- 拟议的风敏感轨迹优化框架和C-GWO+算法为精确的城市流行病预防提供了强大的低碳解决方案.
- 综合方法有效地平衡了覆盖范围,沉积均性和在具有挑战性的风条件下遵守安全要求.
- 这项研究在无人机公共卫生干预方面取得了重大进展,特别是在城市环境中对载体传播疾病的控制.
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