在无基础设施的多机器人相对本地化中利用冗余进行UWB异常检测.
Sahar Salimpour1, Paola Torrico Morón1, Xianjia Yu1
1Turku Intelligent Embedded and Robotic Systems (TIERS) Lab, University of Turku, Turku, Finland.
Frontiers in robotics and AI
|January 10, 2024
概括
这项研究通过检测和消除范围异常来增强多机器人系统的超宽带 (UWB) 定位. 这提高了合作本地化的弹性,而没有显著的计算成本.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 定位和导航 定位和导航
背景情况:
- 超宽带 (UWB) 定位在没有全球导航卫星系统 (GNSS) 的环境中提供了具有成本效益和准确的定位.
- 目前关于UWB弹性的研究主要集中在非视线和多路检测上,对多节点相对定位干扰的关注有限.
研究的目的:
- 开发一种方法来检测多机器人合作本地化系统内的UWB范围测量中的范围异常.
- 在多机器人系统中增强相对定位的弹性,以应对距离干扰.
主要方法:
- 利用相对本地化中的冗余性,通过使用不同的数据子集计算每个节点的位置.
- 识别和消除在合作本地化框架内表现出范围异常的节点的影响.
主要成果:
- 拟议的方法有效地识别了具有范围异常的节点,例如因时间错误或硬件故障引起的异常.
- 这种方法导致了一个更有弹性的合作本地化框架.
结论:
- 开发的方法增强了多机器人系统中基于UWB的合作本地化的稳定性.
- 异常检测技术是通用的,可以扩展到各种类型的范围干扰与微不足道的计算开销.
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