一个平面多惯性导航策略,用于信号可变环境的自主系统.
Wenbin Dong1,2, Cheng Lu2, Le Bao1
1Department of Mechatronics Engineering, Hanyang University, Ansan 15588, Republic of Korea.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
一个新的多惯性导航系统 (M-INSs) 通过集成多个INS单元和扩展的卡尔曼波器 (EKF) 来改善移动机器人的动态定位. 该系统可将定位错误减少60%以上,从而提高机器人导航的准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 导航系统 导航系统
- 传感器融合式传感器
背景情况:
- 传统的单一惯性导航系统 (INS) 存在累积传感器错误,特别是在动态条件下,这限制了移动机器人的精度.
- 精确的动态定位对于先进的移动机器人应用,如自动驾驶和灾难响应至关重要.
研究的目的:
- 开发和验证一个多惯性导航系统 (M-INSs) 算法,以克服单个INS单元在动态定位方面的局限性.
- 显著提高在动态环境中运行的移动机器人的定位精度和稳定性.
主要方法:
- 开发了一种新的算法,将多个INS单元集成到平面配置中,使用固定的单元间距离作为不变约束.
- 扩展卡尔曼波器 (EKF) 用于传感器数据融合和状态估计,以提高定位精度.
- 进行了动态实验验证,以评估拟议的3INS EKF算法的性能与单个INS单位的性能.
主要成果:
- 与单个INS单元相比,多INS EKF算法在定位准确性和稳定性方面取得了明显的改进.
- 定位错误显著减少,平均准确度提高率超过60%.
- 该系统在缓解在动态条件下传统INS固有的累积传感器错误方面被证明是有效的.
结论:
- 开发的使用EKF的M-INS为移动机器人的精确动态定位提供了强大的解决方案.
- 这一进步为高精度移动机器人应用提供了关键的改进,包括自动驾驶和搜索和救援.
- 这项研究为通过多传感器集成和先进的过技术增强机器人导航系统开辟了新的研究途径.
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