Robotics has dramatically increased our ability to gather data about our environments, creating an opportunity for the robotics and algorithms communities to collaborate on novel solutions to environmental monitoring problems. To understand a taxonomy of problems and methods in this realm, we present the first comprehensive survey of decision-theoretic approaches that enable efficient sampling of various environmental processes. We investigate representations for different environments, followed by a discussion of using these presentations to solve tasks of interest, such as learning, localization, and monitoring. To efficiently implement the tasks, decision-theoretic optimization algorithms consider: (1) where to take measurements from, (2) which tasks to be assigned, (3) what samples to collect, (4) when to collect samples, (5) how to learn environment; and (6) who to communicate. Finally, we summarize our study and present the challenges and opportunities in robotic environmental monitoring.

翻译：机器人技术极大地提升了我们从环境中采集数据的能力，为机器人学与算法领域合作解决环境监测问题创造了机遇。为深入理解该领域的问题与方法分类体系，我们首次系统综述了基于决策论的高效环境过程采样方法。首先探究了不同环境的表征方式，继而讨论如何利用这些表征来解决学习、定位、监测等关键任务。为实现高效任务执行，决策论优化算法需考虑以下要素：(1) 测量点的选取位置，(2) 任务分配方案，(3) 样本采集内容，(4) 采集时间策略，(5) 环境学习机制，以及(6) 通信对象选择。最后，我们总结了研究成果，并剖析了机器人环境监测面临的挑战与发展机遇。