GPUサンプリングベース確率予測制御による脚式移動の利点
On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion
GPU加速のサンプリングベース確率制御を四足歩行ロボットに適用し、歩容周波数適応を実現して外乱下で従来の勾配ベースMPCを上回ることを実機で示した。
著者: Giulio Turrisi, Valerio Modugno, Lorenzo Amatucci, Dimitrios Kanoulas, Claudio Semini
分類: cs.RO
原文アブストラクト
Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addressed. In this paper, we introduce the use of Sample-Based Stochastic control strategies for quadrupedal robots, as an alternative to traditional optimal control laws. We show that Sample-Based Stochastic methods, supported by GPU acceleration, can be effectively applied to real quadruped robots. In particular, in this work, we focus on achieving gait frequency adaptation, a notable challenge in quadrupedal locomotion for gradient-based methods. To validate the effectiveness of Sample-Based Stochastic controllers we test two distinct approaches for quadrupedal robots and compare them against a conventional gradient-based Model Predictive Control system. Our findings, validated both in simulation and on a real 21Kg Aliengo quadruped, demonstrate that our method is on par with a traditional Model Predictive Control strategy when the robot is subject to zero or moderate disturbance, while it surpasses gradient-based methods in handling sustained external disturbances, thanks to the straightforward gait adaptation strategy that is possible to achieve within their formulation.
