The present study addresses the challenge of effectively deploying a multi-robot team to optimally cover a domain with unknown density distribution. Specifically, we propose a distribute coverage-based control algorithm that enables a group of autonomous robots to simultaneously learn and estimate a spatial field over the domain. Additionally, we consider a scenario where the robots are deployed in a noisy environment or equipped with noisy sensors. To accomplish this, the control strategy utilizes Gaussian Process Regression (GPR) to construct a model of the monitored spatial process in the environment. Our strategy tackles the computational limits of Gaussian processes (GPs) when dealing with large data sets. The control algorithm filters the set of samples, limiting the GP training data to those that are relevant to improving the process estimate, avoiding excessive computational complexity and managing the noise in the observations. To evaluate the effectiveness of our proposed algorithm, we conducted several simulations and real platform experiments.

中文翻译：

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带噪声观测的空间过程估计的分布式覆盖控制

本研究解决了有效部署多机器人团队以最佳地覆盖密度分布未知的域的挑战。具体来说，我们提出了一种基于分布式覆盖的控制算法，使一组自主机器人能够同时学习和估计域上的空间场。此外，我们考虑了机器人部署在嘈杂环境中或配备嘈杂传感器的场景。为了实现这一目标，控制策略利用高斯过程回归（GPR）来构建环境中监测的空间过程的模型。我们的策略解决了处理大型数据集时高斯过程（GP）的计算限制。控制算法过滤样本集，将 GP 训练数据限制为与改进过程估计相关的数据，避免过多的计算复杂性并管理观测中的噪声。为了评估我们提出的算法的有效性，我们进行了多次模拟和真实平台实验。