用自研足部传感器+LSTM实时检测机器人滑移，提前发现微小滑动。
SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots
- 融合多模态传感与LSTM模型，从地面反作用力中识别滑移异常
- 在24.1±6.4mm微小位移时即检出滑移，准确率达85.9%
- 适合需高稳定性行走的四足机器人，尤其在湿滑地形下
四足机器人依赖对地面交互的精确感知以穿越复杂地形，如湿滑表面。现有滑移检测方法多基于运动学和本体感知，难以捕捉灾难性失稳前的早期滑移。本文提出SlipSense框架，通过为四足机器人设计的轻量化传感器足部，实现基于力的在线滑移检测。该框架结合多模态传感器设计与基于LSTM的模型，推断地面反作用力并识别运动过程中的滑移异常。系统部署于Unitree Go1四足机器人，在湿滑地形上实现盲态在线滑移检测。方法可于平均位移24.1±6.4mm时检测早期滑移，总体准确率达85.9%，相较采用状态估计推导足部速度的标准运动学基线，检测分辨率提升3.3倍，准确率相对提高24%。本工作为足式机器人力感知步态自适应提供基础，使未来控制器能估计地形摩擦并调整约束，从而提升系统整体稳定性。
原文摘要 · Abstract (English)
Legged robots rely on accurate ground interaction awareness to traverse variable terrains, such as slippery surfaces. Existing slip detection methods often rely on kinematics and proprioception, which lack the sensitivity to detect early-stage slips that occur prior to catastrophic instability. Thus, this paper presents SlipSense, a novel framework for online force-based slip detection using a custom lightweight sensorized foot for quadrupeds to detect slip. The framework integrates a multimodal sensor design with a LSTM-based model to infer ground reaction forces and detect slip-indicative anomalies during locomotion. The proposed framework is deployed on a Unitree Go1 quadruped to demonstrate blind online slip detection over a slippery terrain. Our method detects early-stage slips down to an average displacement of 24.1 +/-6.4mm with an overall accuracy of 85.9%. This represents a 3.3-fold finer detection resolution and a 24% relative accuracy improvement over a standard kinematic baseline that uses foot velocity inferred through state estimation. The work in this paper serves as a foundation for force-aware gait adaptation in legged robotic locomotion, allowing future controllers to estimate terrain friction and adjust constraints, thus improving the overall stability of the system.
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