Multi-robot active sensing of non-stationary gaussian process-based environmental phenomena
Multi-robot active sensing of non-stationary gaussian process-based environmental phenomena
复制标题
基于非平稳高斯过程的环境现象的多机器人主动感知
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Patrick Jaillet
中科院分区：
文献类型：
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作者：
Ruofei Ouyang;K. H. Low;Jie Chen;Patrick Jaillet
环境感知和监测的一个关键挑战是感知、建模和预测大规模、空间相关的环境现象，特别是当它们是未知和非平稳的。本文提出了一种分散的多机器人主动感知（decs - mas）算法，该算法可以有效地协调多个机器人的探索，以收集最丰富的观测信息，以预测未知的非平稳现象。通过使用狄利克雷过程混合高斯过程（DPM-GPs）对这一现象进行建模，我们在这里的工作是新颖的，它展示了如何利用DPM-GPs及其结构特性来(a)形式化一个主动感知标准，该标准在收集最具信息量的观测值以估计未知的、非平稳的空间相关结构与预测给定当前的、不精确的相关结构估计的现象之间进行权衡；(b)支持有效的分散协调。本文还对decc - mas的性能提供了理论保证，并对其时间复杂度进行了分析。我们使用两个真实世界的数据集实证证明，DEC-MAS优于最先进的MAS算法。
A key challenge of environmental sensing and monitoring is that of sensing, modeling, and predicting large-scale, spatially correlated environmental phenomena, especially when they are unknown and non-stationary. This paper presents a decentralized multi-robot active sensing (DEC-MAS) algorithm that can efficiently coordinate the exploration of multiple robots to gather the most informative observations for predicting an unknown, non-stationary phenomenon. By modeling the phenomenon using a Dirichlet process mixture of Gaussian processes (DPM-GPs), our work here is novel in demonstrating how DPM-GPs and its structural properties can be exploited to (a) formalize an active sensing criterion that trades off between gathering the most informative observations for estimating the unknown, non-stationary spatial correlation structure vs. that for predicting the phenomenon given the current, imprecise estimate of the correlation structure, and (b) support efficient decentralized coordination. We also provide a theoretical performance guarantee for DEC-MAS and analyze its time complexity. We empirically demonstrate using two real-world datasets that DEC-MAS outperforms state-of-the-art MAS algorithms.