可変慣性モデル予測制御による高速二足歩行マニューバ
Variable Inertia Model Predictive Control for Fast Bipedal Maneuvers
重心慣性が変化することを考慮したMPCを凸最適化で定式化し、遊脚軌道から慣性を予測するネットワークと全身制御を組み合わせて、ヒューマノイドの高速で安定した歩行を実現した。
著者: Seung Hyeon Bang, Jaemin Lee, Carlos Gonzalez, Luis Sentis
分類: cs.RO
原文アブストラクト
This paper proposes a novel control framework for agile and robust bipedal locomotion, addressing model discrepancies between full-body and reduced-order models. Specifically, assumptions such as constant centroidal inertia have introduced significant challenges and limitations in locomotion tasks. To enhance the agility and versatility of full-body humanoid robots, we formalize a Model Predictive Control (MPC) problem that accounts for the variable centroidal inertia of humanoid robots within a convex optimization framework, ensuring computational efficiency for real-time operations. In the proposed formulation, we incorporate a centroidal inertia network designed to predict the variable centroidal inertia over the MPC horizon, taking into account the swing foot trajectories -- an aspect often overlooked in ROM-based MPC frameworks. By integrating the MPC-based contact wrench planning with our low-level whole-body controller, we significantly improve the locomotion performance, achieving stable walking at higher velocities that are not attainable with the baseline method. The effectiveness of our proposed framework is validated through high-fidelity simulations using our full-body bipedal humanoid robot DRACO 3, demonstrating dynamic behaviors.
