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 letter, 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.

中文翻译：

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用于多机器人协调的 Stein 变分置信传播

多机器人系统的分散协调涉及在具有挑战性的高维空间中进行规划。当存在障碍物和不同的不确定性来源（例如不准确的动态模型和传感器噪声）时，规划问题尤其具有挑战性。在这封信中，我们介绍了斯坦因变分置信传播（SVBP），这是一种用于对图中节点的非参数边缘分布进行推理的新颖算法。我们通过将机器人群建模为图形模型并对每个机器人进行推理，将 SVBP 应用于多机器人协调。我们在模拟多机器人感知任务以及模型预测控制（MPC）框架内的多机器人规划任务上（在模拟和现实世界的移动机器人上）展示了我们的算法。我们的实验表明，SVBP 比基于采样或高斯基线更好地表示多模态分布，从而提高了感知和规划任务的性能。此外，我们还表明，SVBP 能够代表分散式多机器人规划的不同轨迹，这使得它比领先基线更不容易出现死锁场景。