实时级联状态估计框架用于使用proprioception的腿类机器人
Botao Liu1, Fei Meng1, Zhihao Zhang1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100811, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
这项研究引入了一种新的机器人状态估计框架, 这种方法提高了腿类机器人的精度和实时性能,特别是在脚与地面接触时.
科学领域:
- 机器人技术
- 国家估计
- 控制系统
背景情况:
- 精确的状态估计对于腿类机器人控制至关重要.
- 自身感知传感器提供了丰富的估计信息来源.
- 现有的方法在移动过程中与冲击动态作斗争.
研究的目的:
- 为使用自身感知的机器人开发一个级联状态估计框架.
- 提高机器人状态估计的准确性和实时性能.
- 为了有效地处理撞击噪声在腿部机器人运动.
主要方法:
- 一个基于通用动量的卡尔曼波器 (GMKF) 估计了地面反应力.
- 一个错误状态卡尔曼波器 (ESKF) 提供先前状态估计.
- 一个移动地平线估计 (MHE) 问题是关于Lie组的,并通过并行实时代 (Para-RTI) 解决.
主要成果:
- 拟议的框架实现了对分组的紧密结合估计.
- 与现有方法相比,已经证明了更高的准确性和实时性能.
- 有效地减轻脚机器人与地面接触时的撞击噪声.
结论:
- 基于级联自知觉的框架为机器人状态估计提供了更好的方法.
- 这种方法对于在动态环境中操作的腿类机器人来说尤其有效.
- 在BQR3机器人上的实验验证证了该框架的有效性.
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