マルチロボット協調のためのStein変分信念伝播
Stein Variational Belief Propagation for Multi-Robot Coordination
グラフ上の非パラメトリック周辺分布を推論するSVBPを提案し、ロボット群をグラフィカルモデルとしてモデル化して分散協調計画に応用、シミュレーションと実機で有効性を示した。
著者: Jana Pavlasek, Joshua Jing Zhi Mah, Ruihan Xu, Odest Chadwicke Jenkins, Fabio Ramos
分類: cs.RO
原文アブストラクト
Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. The planning problem is particularly challenging in the presence of obstacles and different sources of uncertainty such as inaccurate dynamic models and sensor noise. In this paper, we introduce Stein Variational Belief Propagation (SVBP), a novel algorithm for performing inference over nonparametric marginal distributions of nodes in a graph. We apply SVBP to multi-robot coordination by modelling a robot swarm as a graphical model and performing inference for each robot. We demonstrate our algorithm on a simulated multi-robot perception task, and on a multi-robot planning task within a Model-Predictive Control (MPC) framework, on both simulated and real-world mobile robots. Our experiments show that SVBP represents multi-modal distributions better than sampling-based or Gaussian baselines, resulting in improved performance on perception and planning tasks. Furthermore, we show that SVBP's ability to represent diverse trajectories for decentralized multi-robot planning makes it less prone to deadlock scenarios than leading baselines.
