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Updated: Jun 12, 2025

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轨迹优化以提高轴承单一目标定位和传感器偏差校准的可观察性
Jicheng Peng1, Qianshuai Wang1, Bingyu Jin2
1School of Automation, Southeast University, Nanjing 210096, China.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
这项研究提高了无人机 (UAV) 目标定位,使用控制屏障功能 (CBF) 进行运动规划. 新方法显著提高了可观测性和本地化准确性,优于现有的算法.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 导航和指导的导航和指导.
- 优化算法 优化算法
背景情况:
- 由于传感器偏差,仅轴承目标定位具有挑战性.
- 现有的可观测性分析方法 (例如,排名标准) 提供有限的定量见解.
- 无人机 (UAV) 运动规划对于提高本地化性能至关重要.
研究的目的:
- 为无人机开发一种新的方法,以提高无人机仅靠轴承定位目标的可观测性.
- 在定位系统中解决传感器偏差污染问题.
- 优化无人机轨迹,以提高本地化准确性和融合.
主要方法:
- 一种基于控制屏障函数 (CBF) 的方法,用于无人机运动规划,灵感来自植物光变.
- 使用条件号码进行定量可观察性分析,以确定关键影响因素.
- 使用非线性受约束多目标灰狼优化算法 (NCMOGWOA) 制定和解决一个多目标,非线性优化问题的方法.
主要成果:
- 条件数量减少了三倍,大大提高了系统的可观测性.
- 拟议的NCMOGWOA算法在定位准确性和融合方面表现出卓越的性能.
- 与其他算法相比,实现了7.3442的最小代距离 (GD) 和8.4577的倒置代距离 (IGD).
- 探索CBF减弱率和初始飞行路径角度对系统性能的影响.
结论:
- 提出的基于CBF的运动规划方法有效地提高了仅轴承目标定位的可观测性.
- NCMOGWOA是一个强大而高效的算法,用于优化复杂场景中的无人机轨迹.
- 该研究在自主导航和目标追踪系统方面取得了重大进展.
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