In this paper, we present algorithms to identify environmental hotspots using mobile sensors. We examine two approaches: one involving a single robot and another using multiple robots coordinated through a decentralized robot system. We introduce an adaptive algorithm that does not require precise knowledge of Gaussian Processes (GPs) hyperparameters, making the modeling process more flexible. The robots operate for a pre-defined time in the environment. The multi-robot system uses Voronoi partitioning to divide tasks and a Monte Carlo Tree Search for optimal path planning. Our tests on synthetic and a real-world dataset of Chlorophyll density from a Pacific Ocean sub-region suggest that accurate estimation of GP hyperparameters may not be essential for hotspot detection, potentially simplifying environmental monitoring tasks.

翻译：本文提出了利用移动传感器识别环境热点区域的算法。我们探讨了两种方法：单机器人方法以及通过分散式机器人系统协调的多机器人方法。我们引入了一种自适应算法，该算法无需精确了解高斯过程（GP）的超参数，从而使建模过程更加灵活。机器人在环境中预定时间内运行。多机器人系统使用Voronoi划分来分配任务，并采用蒙特卡洛树搜索进行最优路径规划。我们在合成数据集以及太平洋子区域叶绿素密度真实数据集上的测试表明，精确估计GP超参数对于热点检测可能并非必需，这有望简化环境监测任务。