連続体ロボットの精密制御のためのアクチュエータ対応スペクトル部分多様体の学習
Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots
制御入力を状態表現に組み込んだ制御拡張スペクトル部分多様体(caSSM)を提案し、腱駆動型連続体ロボットの高次元非線形ダイナミクスを低次元モデル化。実機で開ループ予測誤差を40%、MPCでの追従誤差を52%削減した。
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著者: Paul Leonard Wolff, Hugo Buurmeijer, Luis Pabon, John Irvin Alora, Mark Leone, Roshan S. Kaundinya, Amirhossein Kazemipour, Robert K. Katzschmann, Marco Pavone
分類: cs.RO
原文アブストラクト
Continuum robots exhibit high-dimensional, nonlinear dynamics which are often coupled with their actuation mechanism. Spectral submanifold (SSM) reduction has emerged as a leading method for reducing high-dimensional nonlinear dynamical systems to low-dimensional invariant manifolds. Our proposed control-augmented SSMs (caSSMs) extend this methodology by explicitly incorporating control inputs into the state representation, enabling these models to capture nonlinear state-input couplings. Training these models relies solely on controlled decay trajectories of the actuator-augmented state, thereby removing the additional actuation-calibration step commonly needed by prior SSM-for-control methods. We learn a compact caSSM model for a tendon-driven trunk robot, enabling real-time control and reducing open-loop prediction error by 40% compared to existing methods. In closed-loop experiments with model predictive control (MPC), caSSM reduces tracking error by 52%, demonstrating improved performance against Koopman and SSM based MPC and practical deployability on hardware continuum robots.
