マルチエージェント軌道計画のためのメッセージパッシングアルゴリズム
A message-passing algorithm for multi-agent trajectory planning
ADMMを改良し、複数エージェントの衝突回避グローバル軌道を並列計算可能に求める手法を提案。局所的な速度空間最適化にも応用できることを示した。
著者: Jose Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan Yedidia
分類: cs.AI, cs.DC, cs.MA, cs.RO, cs.SY, eess.SY
原文アブストラクト
We describe a novel approach for computing collision-free \emph{global} trajectories for $p$ agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM). Compared with existing methods, our approach is naturally parallelizable and allows for incorporating different cost functionals with only minor adjustments. We apply our method to classical challenging instances and observe that its computational requirements scale well with $p$ for several cost functionals. We also show that a specialization of our algorithm can be used for {\em local} motion planning by solving the problem of joint optimization in velocity space.
