マルチロボットシステムのための分散最適化手法サーベイ
A Survey of Distributed Optimization Methods for Multi-Robot Systems
複数ロボットの分散最適化アルゴリズムを分類・評価し、3つの典型問題のシミュレーションで比較した。C-ADMMが特に有用であることを示した。
著者: Trevor Halsted, Ola Shorinwa, Javier Yu, Mac Schwager
分類: cs.RO
原文アブストラクト
Distributed optimization consists of multiple computation nodes working together to minimize a common objective function through local computation iterations and network-constrained communication steps. In the context of robotics, distributed optimization algorithms can enable multi-robot systems to accomplish tasks in the absence of centralized coordination. We present a general framework for applying distributed optimization as a module in a robotics pipeline. We survey several classes of distributed optimization algorithms and assess their practical suitability for multi-robot applications. We further compare the performance of different classes of algorithms in simulations for three prototypical multi-robot problem scenarios. The Consensus Alternating Direction Method of Multipliers (C-ADMM) emerges as a particularly attractive and versatile distributed optimization method for multi-robot systems.
