Adaptive Sampling and Online Learning in Multi-Robot Sensor Coverage with Mixture of Gaussian Processes
Adaptive Sampling and Online Learning in Multi-Robot Sensor Coverage with Mixture of Gaussian Processes
复制标题
混合高斯过程的多机器人传感器覆盖范围内的自适应采样和在线学习
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
K. Sycara
中科院分区：
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作者：
Wenhao Luo;K. Sycara
我们考虑了多机器人传感器覆盖的在线环境采样和建模问题，其中一组机器人分散在工作空间中，以优化整体感知性能。与现有的大多数多机器人覆盖控制工作假设环境现象分布的先验知识(也称为密度函数)不同，我们放宽了这一假设，使机器人团队能够使用自适应采样和非参数推理(如高斯过程)在线有效地学习未知密度函数的模型。为了捕捉环境现象中明显不同的成分，我们提出了一种混合局部学习高斯过程的集体模型学习方法，并提出了多机器人覆盖下同时自适应采样的信息论准则。我们的方法证明了环境建模的更好的普适性，从而在不假设密度函数是先验已知的情况下改善了覆盖性能。通过对室内静态传感器信息采集的仿真，验证了该算法的有效性。
We consider the problem of online environmental sampling and modeling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the overall sensing performance. In contrast to most existing works on multi-robot coverage control that assume prior knowledge of the distribution of environmental phenomenon, also known as density function, we relax this assumption and enable the robot team to efficiently learn the model of the unknown density function Online using adaptive sampling and non-parametric inference such as Gaussian Process (GP). To capture significantly different components of the environmental phenomenon, we propose a new approach with mixture of locally learned Gaussian Processes for collective model learning and an information-theoretic criterion for simultaneous adaptive sampling in multi-robot coverage. Our approach demonstrates a better generalization of the environment modeling and thus the improved performance of coverage without assuming the density function is known a priori. We demonstrate the effectiveness of our algorithm via simulations of information gathering from indoor static sensors.