動的脚式移動のためのKoopman埋め込みに基づく重心状態推定
Centroidal State Estimation based on the Koopman Embedding for Dynamic Legged Locomotion
Koopman作用素理論と動的モード分解・深層学習で脚式ロボットの非線形ダイナミクスを線形化し、凸二次計画問題として定式化した移動ホライズン推定器で重心状態をリアルタイム推定する手法を提案。
著者: Shahram Khorshidi, Murad Dawood, Maren Bennewitz
分類: cs.RO
原文アブストラクト
In this paper, we introduce a novel approach to centroidal state estimation, which plays a crucial role in predictive model-based control strategies for dynamic legged locomotion. Our approach uses the Koopman operator theory to transform the robot's complex nonlinear dynamics into a linear system, by employing dynamic mode decomposition and deep learning for model construction. We evaluate both models on their linearization accuracy and capability to capture both fast and slow dynamic system responses. We then select the most suitable model for estimation purposes, and integrate it within a moving horizon estimator. This estimator is formulated as a convex quadratic program to facilitate robust, real-time centroidal state estimation. Through extensive simulation experiments on a quadruped robot executing various dynamic gaits, our data-driven framework outperforms conventional Extended Kalman Filtering technique based on nonlinear dynamics. Our estimator addresses challenges posed by force/torque measurement noise in highly dynamic motions and accurately recovers the centroidal states, demonstrating the adaptability and effectiveness of the Koopman-based linear representation for complex locomotive behaviors. Importantly, our model based on dynamic mode decomposition, trained with two locomotion patterns (trot and jump), successfully estimates the centroidal states for a different motion (bound) without retraining.
