多策略改进的红尾算法用于现实环境无人驾驶飞行器路径规划
Mingen Wang1, Panliang Yuan2, Pengfei Hu1
1Laboratory for Robot Mobility Localization and Scene Deep Learning Technology, Guizhou Equipment Manufacturing Polytechnic, Guiyang 550025, China.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
本研究介绍了一种改进的红尾算法,用于无人机 (UAV) 路径规划. 这种新方法增强了探索和利用,使无人机在现实环境中更安全,更有效地导航.
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:
- 无人机技术对于监视,搜救和环境监测至关重要.
- 无人机在现实环境中的可靠,安全和经济的路径规划对无人机构成了重大挑战.
研究的目的:
- 为无人机路径规划提出一种新的多策略改进的红尾 (IRTH) 算法.
- 提高无人机路径规划算法的探索和利用能力.
主要方法:
- 实现基于伯努利映射的随机反向学习,用于初始人口增强.
- 利用动态位置更新优化与随机平均值融合来改善探索.
- 引入了基于信任域的优化,用于边境位置更新,以平衡勘探和开采.
主要成果:
- 该IRTH算法在IEEE CEC2017测试集中的其他11个算法相比,表现出了竞争力.
- 统计分析证实了显著的差异,突出了算法的有效性.
- 在实际的无人机路径规划场景中,IRTH算法取得了更好的结果.
结论:
- 拟议的IRTH算法是有效的无人机路径规划在现实世界中环境.
- 多策略增强可以提高勘探,开采和整体路径规划的性能.
- IRTH算法为复杂的无人机导航任务提供了可行的解决方案.
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