Distributed Environmental Modeling and Adaptive Sampling for Multi-Robot Sensor Coverage
Distributed Environmental Modeling and Adaptive Sampling for Multi-Robot Sensor Coverage
复制标题
多机器人传感器覆盖的分布式环境建模和自适应采样
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
K. Sycara
中科院分区：
文献类型：
--
作者：
Wenhao Luo;Changjoo Nam;G. Kantor;K. Sycara
我们考虑多机器人传感器覆盖的在线分布式环境建模和自适应采样问题，其中一组机器人分布在工作空间中，以优化环境现象的传感性能，其分布通常被称为密度函数。与大多数现有的工作不同，这些工作要么预先假设密度函数的某些知识，要么假设从所有机器人收集的数据具有全局知识来集中学习密度函数，我们提出了一种完全分布式自适应采样方法，使机器人能够有效地在线学习未知的密度函数。特别是，我们基于学习的密度函数开发了自适应覆盖控制器，以最大限度地降低传感成本。为了在全局知识不可用时仅使用每个机器人本地收集的数据来捕获环境现象的显着不同组成部分，我们提出了一种分布式混合高斯过程算法，该算法使机器人能够通过仅交换模型相关参数来协作学习全局密度函数。我们通过评估从农田机器人和室内静态传感器收集的真实数据，实证证明了我们算法的有效性。
We consider the problem of online distributed environmental modeling and adaptive sampling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the sensing performance over environmental phenomena, whose distribution is often referred to as a density function. Unlike most existing works that either assume certain knowledge of the density function beforehand or centrally learn the density function assuming global knowledge of collected data from all the robots, we propose a fully distributed adaptive sampling approach to allow robots to efficiently learn the unknown density function online. In particular, we developed adaptive coverage controllers based on the learned density functions for minimizing the sensing cost. To capture significantly different components of the environmental phenomenon with only locally collected data for each robot when global knowledge is not available, we propose a distributed mixture of Gaussian Processes algorithm that enables robots to collaboratively learn the global density function by exchanging only model-related parameters. We empirically demonstrate the effectiveness of our algorithm via evaluation on real-world data gathered from agricultural field robot and indoor static sensors.