在线识别机器人在滑地上的摩擦系数，提升运动稳定性。
Online Friction Coefficient Identification for Legged Robots on Slippery Terrain Using Smoothed Contact Gradients
- 用平滑接触梯度优化摩擦系数，解决非光滑动力学导致的梯度失效问题。
- 实验显示在多种初始条件下都能快速准确识别摩擦系数。
- 适合需要实时适应复杂地形的四足机器人研究者使用。
本文提出一种针对腿式机器人在滑地环境中在线识别摩擦系数的框架。该方法将优化问题定义为最小化实际状态与由摩擦系数参数化的预测状态之间的残差之和，基于刚体接触动力学建模。特别地，该框架利用通过平滑库仑摩擦的互补条件所获得的解析平滑接触冲量梯度，解决了非光滑接触动力学带来的无效梯度问题。此外，引入剔除法，在接触初始化后过滤高法向接触速度的数据，以提高识别精度。为验证该框架的有效性，我们在四足机器人平台KAIST HOUND上于滑地和非滑地环境中进行了实验。结果表明，该方法在不同初始条件下均能实现快速且一致的摩擦系数识别。
原文摘要 · Abstract (English)
This paper proposes an online friction coefficient identification framework for legged robots on slippery terrain. The approach formulates the optimization problem to minimize the sum of residuals between actual and predicted states parameterized by the friction coefficient in rigid body contact dynamics. Notably, the proposed framework leverages the analytic smoothed gradient of contact impulses, obtained by smoothing the complementarity condition of Coulomb friction, to solve the issue of non-informative gradients induced from the nonsmooth contact dynamics. Moreover, we introduce the rejection method to filter out data with high normal contact velocity following contact initiations during friction coefficient identification for legged robots. To validate the proposed framework, we conduct the experiments using a quadrupedal robot platform, KAIST HOUND, on slippery and nonslippery terrain. We observe that our framework achieves fast and consistent friction coefficient identification within various initial conditions.
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