連結行列リー群上の誤差状態モデル予測制御による脚式ロボット制御
An Error-State Model Predictive Control on Connected Matrix Lie Groups for Legged Robot Control
リー代数上で線形化した誤差追従ダイナミクスを用い、回転と位置の同時収束を高速化する新しい誤差状態MPCを提案し、四足ロボットの姿勢・歩行制御で検証した。
著者: Sangli Teng, Dianhao Chen, William Clark, Maani Ghaffari
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper reports on a new error-state Model Predictive Control (MPC) approach to connected matrix Lie groups for robot control. The linearized tracking error dynamics and the linearized equations of motion are derived in the Lie algebra. Moreover, given an initial condition, the linearized tracking error dynamics and equations of motion are globally valid and evolve independently of the system trajectory. By exploiting the symmetry of the problem, the proposed approach shows faster convergence of rotation and position simultaneously than the state-of-the-art geometric variational MPC based on variational-based linearization. Numerical simulation on tracking control of a fully-actuated 3D rigid body dynamics confirms the benefits of the proposed approach compared to the baselines. Furthermore, the proposed MPC is also verified in pose control and locomotion experiments on a quadrupedal robot MIT Mini Cheetah.
