无人驾驶飞行器集成导航在多种不确定性下进行协作
Le Zhang1,2, Xiaomeng Cao3, Mudan Su1
1Laboratory of Smart Earth, Beijing Institute of Tracking and Telecommunication Technology, Beijing 100094, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种用于无人驾驶飞行器 (UAV) 群体的新型综合导航方法. 该方法有效地建模,传播和减轻不确定性,以实现强大而准确的群体本地化.
科学领域:
- 机器人技术和自主系统
- 导航和控制 导航和控制
- 数据融合数据融合
背景情况:
- 无人驾驶飞行器 (UAV) 群在复杂的任务中提供了显著的优势.
- 准确的导航和定位对于无人机群运作至关重要.
- 在群体中共享信息增强了本地化,但引入了不确定性传播的挑战.
研究的目的:
- 为无人机群群开发一个集成的导航和定位方法,以解决不确定性传播问题.
- 在有噪音数据的情况下,提高群集定位的稳定性和准确性.
- 通过先进的导航技术,提高无人机群体的整体性能.
主要方法:
- 为非线性系统使用正常马分布模型处理异常值的一种强大的集成导航方法.
- 开发统计线性化模型,以解释非线性系统中的不确定性传播.
- 应用增强测量非线性最小平方定位方法,以提高定位精度.
主要成果:
- 拟议的方法有效地模拟,传播和减轻群体导航固有的各种不确定性.
- 正常马分布模型增强了对外部校正数据中异常值的稳定性.
- 模拟实验证实了该方法的强大跟踪能力和无人机群的准确定位能力.
结论:
- 集成的导航和定位方法为无人机群定位提供了强大的解决方案.
- 对多种不确定性的准确计算显著改善了跟踪和定位性能.
- 这种方法提高了无人机群体在各种应用中的可靠性和有效性.
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