環境適応型モデル予測制御による脚式ロコモーション
Model Predictive Control with Environment Adaptation for Legged Locomotion
脚式ロボット向けに脚の可動性を最大化するコストを導入した実時間NMPCを提案し、段差や粗い地形への適応と外乱除去を実験で示した。
著者: Niraj Rathod, Angelo Bratta, Michele Focchi, Mario Zanon, Octavio Villarreal, Claudio Semini, Alberto Bemporad
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Re-planning in legged locomotion is crucial to track the desired user velocity while adapting to the terrain and rejecting external disturbances. In this work, we propose and test in experiments a real-time Nonlinear Model Predictive Control (NMPC) tailored to a legged robot for achieving dynamic locomotion on a variety of terrains. We introduce a mobility-based criterion to define an NMPC cost that enhances the locomotion of quadruped robots while maximizing leg mobility and improves adaptation to the terrain features. Our NMPC is based on the real-time iteration scheme that allows us to re-plan online at $25\,\mathrm{Hz}$ with a prediction horizon of $2$ seconds. We use the single rigid body dynamic model defined in the center of mass frame in order to increase the computational efficiency. In simulations, the NMPC is tested to traverse a set of pallets of different sizes, to walk into a V-shaped chimney,and to locomote over rough terrain. In real experiments, we demonstrate the effectiveness of our NMPC with the mobility feature that allowed IIT's $87\, \mathrm{kg}$ quadruped robot HyQ to achieve an omni-directional walk on flat terrain, to traverse a static pallet, and to adapt to a repositioned pallet during a walk.
