论文标题
动态刚性表面上的腿部类人体运动的不变过滤
Invariant Filtering for Legged Humanoid Locomotion on Dynamic Rigid Surfaces
论文作者
论文摘要
在动态刚性表面（DRS）上对腿部运动的状态估计，这是在世界框架中移动的刚体表面（例如，船舶，飞机和火车），仍然是一个不足的问题。本文引入了一个不变的扩展卡尔曼滤波器，该滤波器通过使用腿部机器人的常见传感器（例如，惯性测量单元（IMU），关节编码器和RDB-D摄像头）来估计机器人在DRS机动期间的姿势和速度。过滤器的一个关键特征在于它明确地解决了非平稳地面表面接触点和混合机器人行为。另一个关键特征是，在没有IMU偏见的情况下，该过滤器满足了有吸引力的群体仿射和不变的观察条件，因此，对于确定性连续阶段而言，滤波器可被证明是收敛的。进行可观察性分析是为了揭示DRS运动对状态可观察性的影响，并检查了混合动力，确定性滤波器系统的收敛性。数字人形机器人在俯仰跑步机上行走的实验验证了在大估计误差和中等DRS运动下提出的滤波器的有效性。可以在以下网址找到实验的视频：https：//youtu.be/scqibfuskzo。
State estimation for legged locomotion over a dynamic rigid surface (DRS), which is a rigid surface moving in the world frame (e.g., ships, aircraft, and trains), remains an under-explored problem. This paper introduces an invariant extended Kalman filter that estimates the robot's pose and velocity during DRS locomotion by using common sensors of legged robots (e.g., inertial measurement units (IMU), joint encoders, and RDB-D camera). A key feature of the filter lies in that it explicitly addresses the nonstationary surface-foot contact point and the hybrid robot behaviors. Another key feature is that, in the absence of IMU biases, the filter satisfies the attractive group affine and invariant observation conditions, and is thus provably convergent for the deterministic continuous phases. The observability analysis is performed to reveal the effects of DRS movement on the state observability, and the convergence property of the hybrid, deterministic filter system is examined for the observable state variables. Experiments of a Digit humanoid robot walking on a pitching treadmill validate the effectiveness of the proposed filter under large estimation errors and moderate DRS movement. The video of the experiments can be found at: https://youtu.be/ScQIBFUSKzo.
