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.

Shake tables provide a critical tool for simulating earthquake events and testing the response of structures to seismic forces. However, existing shake tables are either expensive or proprietary. This paper presents the design and implementation of a low-cost, open-source shake table named Shakebot for earthquake engineering research and education, built using Robot Operating System (ROS) and robotic concepts. The Shakebot adapts affordable and high-accuracy components from 3D printers, particularly a closed-loop stepper motor for actuation and a toothed belt for transmission. The stepper motor enables the bed to reach a maximum horizontal acceleration of 11.8 m/s^2 (1.2 g), and velocity of 0.5 m/s, with a 2 kg specimen. The Shakebot is equipped with an accelerometer and a high frame-rate camera for bed motion estimation. The low cost and easy use make the Shakebot accessible to a wide range of users, including students, educators, and researchers in low-resource settings. An important application of the Shakebot is to examine the dynamics of precariously balanced rocks (PBRs), which are negative indicators of earthquakes in nature. Our earlier research built a virtual shake robot in simulation for the PBR study. The Shakebot provides an approach to validate the simulation through physical experiments. The ROS-based perception and motion software facilitates the code transition from our virtual shake robot to the physical Shakebot. The reuse of the control programs ensures that the implemented ground motions are consistent for both the simulation and physical experiments, which is critical to validate our simulation experiments.