EAB-BES:高密度城市环境中高效无人机路线规划的全球优化方法
Yunhui Zhang1,2, Wenhong Xiao1,2, Shihong Yin3
1School of Internet, Jiaxing Vocational and Technical College, Jiaxing 314036, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
这项研究引入了城市地区3D无人机路径规划的增强式搜索算法 (EAB-BES). EAB-BES算法显著提高了接近速度,并产生了最佳的,无碰撞的飞行路径.
科学领域:
- 机器人和自动化
- 人工智能
- 航空航天工程
背景情况:
- 传统的白搜索 (BES) 算法存在缓慢的融合和局部优化问题.
- 复杂的城市环境对无人驾驶飞行器 (UAV) 路径规划构成重大挑战.
- 现有的算法通常在密集的城市环境中难以适应和高效.
研究的目的:
- 为改善3D无人机路径规划开发一个多策略增强的搜索算法 (EAB-BES).
- 解决城市环境中传统BES算法的局限性.
- 为了提高对接速度,路径准确性和无人机导航的适应性.
主要方法:
- 精英的基于对抗的学习为增强的解决方案空间探索.
- 适应性权重机制以平衡全球搜索和本地利用.
- 基于区块的精英引导的差异性突变用于精细的本地搜索.
主要成果:
- EAB-BES表现出最快的融合速度和最低的稳定适应性值.
- 算法生成了最短,最平滑,最无碰撞的3D路径.
- 在六个高密度城市环境中进行的比较实验证实了对九个先进算法的优越性能.
结论:
- 对于无人机路径规划,EAB-BES比传统的BES算法提供了显著的改进.
- 改进的算法为复杂的城市环境中的自主导航提供了高效,可靠和强大的解决方案.
- 统计分析证实了EAB-BES的卓越表现和竞争力.
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