慣性形状化を活用した俊敏な四足歩行のための運動量考慮型軌道最適化と制御
Momentum-Aware Trajectory Optimization and Control for Agile Quadrupedal Locomotion
重心ダイナミクスと全身軌道最適化を交互に解く階層型オフライン計画と、重心ダイナミクスの線形変換に基づく凸MPCを組み合わせ、四足ロボットのアクロバティックな動きを実現した研究。
著者: Ziyi Zhou, Bruce Wingo, Nathan Boyd, Seth Hutchinson, Ye Zhao
分類: cs.RO
原文アブストラクト
In this letter, we present a versatile hierarchical offline planning algorithm, along with an online control pipeline for agile quadrupedal locomotion. Our offline planner alternates between optimizing centroidal dynamics for a reduced-order model and whole-body trajectory optimization, with the aim of achieving dynamics consensus. Our novel momentum-inertia-aware centroidal optimization, which uses an equimomental ellipsoid parameterization, is able to generate highly acrobatic motions via ``inertia shaping". Our whole-body optimization approach significantly improves upon the quality of standard DDP-based approaches by iteratively exploiting feedback from the centroidal level. For online control, we have developed a novel convex model predictive control scheme through a linear transformation of the full centroidal dynamics. Our controller can efficiently optimize for both contact forces and joint accelerations in single optimization, enabling more straightforward tracking for momentum-rich motions compared to existing quadrupedal MPC controllers. We demonstrate the capability and generality of our trajectory planner on four different dynamic maneuvers. We then present one hardware experiment on the MIT Mini Cheetah platform to demonstrate the performance of the entire planning and control pipeline on a twisting jump maneuver.
