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一个改进的PSO-ABC无人机路径规划算法,基于城市空域拓结构和实际的GIS数据
Yang Liu1, Hailong Dong2, Huayu Liu2
1School of Information Science & Electrical Engineering, Shandong Jiaotong University, Jinan, 250357, China. ly0314@buaa.edu.cn.
Scientific reports
|January 12, 2026
概括
本研究介绍了一种改进的无人飞行器 (UAV) 路径规划算法,使用了新的城市空域拓. 该算法增强了路径流性和融合速度,以实现更安全的无人机操作.
科学领域:
- 机器人和自动化 机器人和自动化
- 航空航天工程 航空航天工程
- 人工智能的人工智能
背景情况:
- 在城市环境中无人驾驶飞行器 (UAV) 的操作需要复杂的路径规划.
- 现有的算法难以应对复杂的城市空域限制和安全考虑.
研究的目的:
- 为无人机提出一个改进的粒子群集优化 - 人工蜂群 (PSO-ABC) 路径规划算法.
- 开发一种方法来构建一个可行的城市空域拓,包括地面风险和可用性.
主要方法:
- 城市空域的分密化成可用性和风险指数的voxels.
- 象限分析和帕雷托分类用于空域可行性评估.
- 在路线规划中,将PSO (全球搜索) 和ABC (本地搜索) 融合在一起.
主要成果:
- 拟议的PSO-ABC算法在路径平滑性和融合速度方面表现出卓越的性能.
- 构建的空域拓有效地整合了城市GIS数据,无人机限制和禁飞区.
- 在真实城市场景中的模拟分析证实了算法的稳定性和稳定性.
结论:
- 开发的城市空域拓和改进的PSO-ABC算法为无人机路径规划提供了强大的解决方案.
- 这种方法提高了无人机在复杂的城市环境中运行的安全性和效率.
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