立体卡尔曼混合共识过器用于协作定位无人驾驶地面车辆集群,随机测量延迟
Weicheng Liu1, Jichao Yang1, Tongbo Xu1
1College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究引入了无人地面车辆 (USV) 集群的新过器,以改善协作本地化,尽管随机测量延迟. 拟议的Cubature Kalman混合共识过器 (CKHCF) 确保分布式USV系统的准确定位.
科学领域:
- 机器人技术和自主系统
- 导航和控制系统 导航和控制系统
- 分布式系统 分布式系统
背景情况:
- 协作本地化对于无人地面车辆 (USV) 集群至关重要,特别是在分散的运营中.
- 随机测量延迟对USV集群定位的准确性和可靠性构成重大挑战.
- 现有的方法经常与缺乏全球通信基础设施的广泛分布的USV集群作斗争.
研究的目的:
- 为USV集群开发一个强大的协作本地化算法,可以处理随机测量延迟.
- 提出一种适用于分散的USV系统的新型Cubature Kalman混合共识过器 (CKHCF).
- 确保拟议的本地化算法的一致性和边界性.
主要方法:
- 为USV集群实施一个立体卡尔曼混合共识过器 (CKHCF).
- 邻近USV之间的信息交换,包括基于距离和相对角度测量的重新计算.
- 重新计算和本地过数据的融合,以增强全球本地化.
- 一步预测技术,以补偿随机测量延迟.
主要成果:
- 拟议的CKHCF算法有效地解决了随机测量延迟的USV集群中的协作本地化问题.
- 数学归纳用于证明CKHCF算法的一致性和局限性.
- 模拟实验证明了开发的算法的卓越性能和有效性.
结论:
- CKHCF算法为USV集群中的分散协作本地化提供了可靠的解决方案.
- 该方法成功地减轻了随机测量延迟对定位准确性的影响.
- 经过验证的算法增强了分布式USV系统的操作能力.
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