相关实验视频
Updated: Jun 6, 2025

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Experimental Methods to Study Human Postural Control
Published on: September 11, 2019
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通过不变扩展卡尔曼波器和干扰观察器在非静止地形上的四方机器人的状态估计
Mingfei Wan1,2,3, Daoguang Liu1,2,3, Jun Wu2,4
1College of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了四足机器人改进的状态估计算法,通过建模脚端滑动和使用不变扩展卡尔曼波器 (InEKF) 与干扰观察器来提高在具有挑战性的地形上的准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 传感器融合式传感器
背景情况:
- 四足机器人擅长复杂的地形,但准确的状态估计对于苛刻的应用至关重要.
- 由于脚端滑动和不稳定的接触,现有的方法与非静止地形作斗争,导致状态漂移.
研究的目的:
- 为在非静止地形上运行的四足机器人开发一个强大的状态估计算法.
- 为了应对脚端滑动带来的挑战,并提高整体状态估计的准确性.
主要方法:
- 一个不变扩展的卡尔曼波器 (InEKF) 与一个干扰观察器集成.
- 模拟脚端滑动作为状态方程内身体速度的偏差.
- 结合脚端速度和位置观测,以及接触概率模型和自适应共变性调整.
主要成果:
- 改进的InEKF算法在非静止地形上的状态估计中表现出更好的稳定性和准确性.
- 观察到有效的补偿脚端滑动和更好的适应不同地形条件.
- 使用Jueying Mini机器人的实验结果验证了该方法对传统过器的优势.
结论:
- 拟议的状态估计方法显著提高四足机器人在具有挑战性的,非静止环境中的性能.
- 滑动建模和自适应观测策略的整合为运动估计提供了更可靠的方法.
- 这项工作有助于推进自主导航和在非结构化的环境中运行有腿机器人的操作.
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