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高了我们收集环境数据的能力，为机器人学与算法领域协作解决环境监测问题创造了契机。为系统梳理该领域的问题与方法分类体系，本文首次全面综述了能够高效采样各类环境过程的决策理论方法。我们首先探讨不同环境的表征方式，进而讨论如何运用这些表征解决学习、定位、监测等关键任务。为高效实施这些任务，决策理论优化算法需考虑以下六个维度：(1) 测量点位选择；(2) 任务分配；(3) 采样方案制定；(4) 采样时机确定；(5) 环境学习策略；以及(6) 通信对象选择。最后，我们总结了现有研究，并展望了机器人环境监测领域面临的挑战与发展机遇。