分散最適化による脚式ロボットのモデル予測制御の高速化
Accelerating Model Predictive Control for Legged Robots through Distributed Optimization
脚式ロボットのモデル予測制御を、ロボットの動力学を並列化可能な部分系に分解しADMMで整合させる分散最適化によって高速化する手法を提案し、集中型全身MPCと同等の解を最大75%短い計算時間で得られることを示した。
著者: Lorenzo Amatucci, Giulio Turrisi, Angelo Bratta, Victor Barasuol, Claudio Semini
分類: cs.RO
原文アブストラクト
This paper presents a novel approach to enhance Model Predictive Control (MPC) for legged robots through Distributed Optimization. Our method focuses on decomposing the robot dynamics into smaller, parallelizable subsystems, and utilizing the Alternating Direction Method of Multipliers (ADMM) to ensure consensus among them. Each subsystem is managed by its own Optimal Control Problem, with ADMM facilitating consistency between their optimizations. This approach not only decreases the computational time but also allows for effective scaling with more complex robot configurations, facilitating the integration of additional subsystems such as articulated arms on a quadruped robot. We demonstrate, through numerical evaluations, the convergence of our approach on two systems with increasing complexity. In addition, we showcase that our approach converges towards the same solution when compared to a state-of-the-art centralized whole-body MPC implementation. Moreover, we quantitatively compare the computational efficiency of our method to the centralized approach, revealing up to a 75% reduction in computational time. Overall, our approach offers a promising avenue for accelerating MPC solutions for legged robots, paving the way for more effective utilization of the computational performance of modern hardware.
