基于水母,粒子群和遗传学的复合改进算法,用于复杂的城市地形中的无人机路径规划
1National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究介绍了一种结合水母搜索和粒子群优化的新算法,用于在复杂的城市环境中无人机 (UAV) 路径规划. 改进的方法提高了安全性,流性和效率,优于传统方法.
科学领域:
- 机器人和自动化机器人与自动化
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 在复杂的城市环境中运行的无人机 (UAV) 航线规划至关重要.
- 现有的算法经常在多障碍场景中与安全性,路径流性和效率作斗争.
- 单个算法可能表现出缓慢的融合和对局部最佳的敏感性.
研究的目的:
- 开发一种创新的复合算法,用于无人机路径规划.
- 在复杂的城市环境中增强路径安全性,流性和最短路径的获取.
- 为了克服个别优化算法的局限性.
主要方法:
- 将水母搜索算法和粒子群算法的集成.
- 开发一个复合改进算法.
- 使用23个基准函数,一个3D城市模型模拟和100个重复实验进行评估.
主要成果:
- 与传统方法相比,复合算法显示出更高的性能.
- 实现了快速融合,高准确性和强大的全球/本地搜索能力.
- 显著减少无人机飞行时间和避难障碍机动.
结论:
- 拟议的算法为UAV在复杂的城市环境中提供了有效的路径规划.
- 它为推进无人机技术提供了强大的技术支持.
- 该方法提高了无人机路径优化,稳定性和运营效率.
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