姿勢を持つロボット：敏捷な脚式ロボットのための特異点のないクォータニオンベースのモデル予測制御
Robots with Attitude: Singularity-Free Quaternion-Based Model-Predictive Control for Agile Legged Robots
脚式ロボットの姿勢表現にクォータニオンを用い、大角度回転時の特異点を回避するモデル予測制御フレームワークを提案。iLQRを拡張し、四足歩行ロボットとヒューマノイドで性能と計算効率を実証した。
著者: Zixin Zhang, John Z. Zhang, Shuo Yang, Zachary Manchester
分類: cs.RO
原文アブストラクト
We present a model-predictive control (MPC) framework for legged robots that avoids the singularities associated with common three-parameter attitude representations like Euler angles during large-angle rotations. Our method parameterizes the robot's attitude with singularity-free unit quaternions and makes modifications to the iterative linear-quadratic regulator (iLQR) algorithm to deal with the resulting geometry. The derivation of our algorithm requires only elementary calculus and linear algebra, deliberately avoiding the abstraction and notation of Lie groups. We demonstrate the performance and computational efficiency of quaternion MPC in several experiments on quadruped and humanoid robots.
