Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots
Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots
复制标题
用一小群机器人持续监测随机时空现象
DOI：
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发表时间：
2014
期刊：
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通讯作者：
Nora Ayanian
中科院分区：
文献类型：
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作者：
S. Garg;Nora Ayanian
本文提出了一种解决方案，用于持续监测现实世界的随机现象，其中底层协方差结构随时间急剧变化，使用少量移动机器人传感器。我们提出了一个自适应解决方案的问题，其中随机现实世界动力学建模为高斯过程（GP）。对潜在协方差结构的信念是从最近观测到的高斯混合物（GM）在GP的低维超参数空间中的动态中学习到的，并使用时序蒙特卡罗方法进行时间调整。每个机器人从GM中抽取一个信念点，通过贪心最大化子模熵函数局部优化一组信息区域。本文的主要贡献有三个方面：利用马尔可夫链蒙特卡罗（MCMC）采样调整了对协方差的信念，使得粒子即使在协方差随时间急剧变化的情况下也能存活；利用信念将熵最大化问题转化为去中心化问题并在连续空间的一组信息区域上发展了一种熵最大化的近似算法。我们通过使用人工数据集和来自固定传感器部署的多个真实数据集的广泛模拟来说明所提出的解决方案的应用，并将其与三种相互竞争的最先进方法进行比较。
This paper presents a solution for persistent monitoring of real-world stochastic phenomena, where the underlying covariance structure changes sharply across time, using a small number of mobile robot sensors. We propose an adaptive solution for the problem where stochastic real-world dynamics are modeled as a Gaussian Process (GP). The belief on the underlying covariance structure is learned from recently observed dynamics as a Gaussian Mixture (GM) in the low-dimensional hyper-parameters space of the GP and adapted across time using Sequential Monte Carlo methods. Each robot samples a belief point from the GM and locally optimizes a set of informative regions by greedy maximization of the submodular entropy function. The key contributions of this paper are threefold: adapting the belief on the covariance using Markov Chain Monte Carlo (MCMC) sampling such that particles survive even under sharp covariance changes across time; exploiting the belief to transform the problem of entropy maximization into a decentralized one; and developing an approximation algorithm to maximize entropy on a set of informative regions in the continuous space. We illustrate the application of the proposed solution through extensive simulations using an artificial dataset and multiple real datasets from fixed sensor deployments, and compare it to three competing state-of-the-art approaches.