基于形多边形区域分解的多无人机森林区域检查路径规划
Bo Kang1, Chuan'an Wang2, Yu Su1
1College of Intelligent Manufacturing, Anhui Science and Technology University, Chuzhou, 239000, China.
Scientific reports
|November 25, 2025
概括
本研究引入了一种新的多无人飞行器 (UAV) 路径规划算法,用于高效的森林监测. 该方法显著减少了覆盖时间和路径长度,改善了生态保护.
科学领域:
- 机器人和自动化机器人与自动化
- 环境监测 环境监测
- 计算机科学 计算机科学
背景情况:
- 传统的森林巡逻方法 (手动,载人飞机) 是低效和昂贵的,特别是在复杂的地形.
- 无人驾驶飞行器 (UAV) 为高效的森林监测提供了一个有希望的替代方案.
- 现有的覆盖路径规划算法与复杂的形区域和多UAV协调扎.
研究的目的:
- 开发一个高效的多无人机覆盖线路规划算法,用于复杂的形森林地区.
- 通过减少巡逻时间和路径长度来提高森林监测效率.
- 为森林地区的生态保护,防火和害虫监测提供强大的解决方案.
主要方法:
- 一个新的算法,结合了空间位置排序和凸分解,用于凸区域分区.
- 一种改进的耳毛切割方法,将复杂的区域分解为凸起的子区域.
- 一个区域分配策略,具有邻近性约束,以实现平衡和连续的多无人机覆盖.
主要成果:
- 与单个无人机操作相比,模拟森林区域测试显示覆盖时间减少了54.7%,路径长度减少了26.3%.
- 使用Xishuangbanna地形数据进行现实世界验证,通过优化飞行高度实现了97.8%的覆盖率,阴影系数为0.09.
- 多无人机战略在最大限度地减少覆盖时间和路径长度,同时确保平衡的任务分配方面超过了现有的方法.
结论:
- 拟议的多UAV覆盖路径规划算法显著提高了森林区域检测效率.
- 该算法为动态森林资源监测和生态保护提供了技术合理的解决方案.
- 这种方法为先进的,自动化的森林监测系统提供了基础.
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