動的剛体面上の脚式ヒューマノイド歩行のための不変フィルタリング
Invariant Filtering for Legged Humanoid Locomotion on Dynamic Rigid Surfaces
船や電車のような動く剛体面(DRS)上を歩くヒューマノイドの姿勢・速度を、IMU・関節エンコーダ・RGB-Dカメラで推定する不変拡張カルマンフィルタを提案し、収束性と可観測性を解析、Digitロボットのピッチングトレッドミル歩行で検証した。
著者: Yuan Gao, Chengzhi Yuan, Yan Gu
分類: cs.RO
原文アブストラクト
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.
