DR-MPC：脚式移動のための高速で実行可能な動力学緩和モデル予測制御
DR-MPC: Fast and Feasible Dynamics-Relaxed Model-Predictive Control for Legged Locomotion
脚式ロボットの歩行制御において、動力学制約を二次ペナルティに緩和し、接触を考慮した入力パラメータ化と専用の内点法ソルバを組み合わせることで、従来手法より大幅に高速なモデル予測制御を実現し、四足歩行ロボットで検証した。
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著者: Run Wang, Alapati Tuerxun, Shuo Liu, Wei Xiao, Ján Drgoňa, Yilin Mo, Liang Wu
分類: cs.RO, eess.SY
原文アブストラクト
This paper presents dynamics-relaxed model predictive control (DR-MPC), a novel MPC formulation for legged locomotion, and a tailored interior-point method (IPM) solver. The formulation combines online optimization feasibility by construction with a contact-aware input parameterization. DR-MPC moves the dynamics equality and affine input constraints into quadratic penalties and retains only nonempty box constraints. The resulting box-constrained quadratic program (QP) has a block-arrow Hessian that enables the state and affine-output directions to be eliminated through a Schur complement. The solver factors only the reduced control system after swing-force elimination and contact-aligned move blocking. For the evaluated implementations using the same DR-MPC formulation, our method achieves median end-to-end MPC speedups of $16.0\times$ over HPIPM and $4.4\times$ over OSQP, with comparable locomotion performance in simulation. DR-MPC achieves a median onboard MPC end-to-end time of $4.4$ ms and is validated on a Unitree Go1 quadruped. Open-source code will be made available after publication.