Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots

In scenarios such as natural disasters, seasonal agriculture, and other short-duration operations, a rapidly deployable, autonomous mobile sensing system that decides where to take sensor measurements can be more versatile and costeffective than installing stationary sensors. In this work, we are interested in formulating a solution for persistent sensing of real-world stochastic phenomena using a team of mobile robots, even when the underlying covariance structure changes sharply across time, such as sunlight variation in a forest understory (Figure 1). Assuming no prior knowledge on the underlying model of the phenomenon dynamics, this presents two challenges: 1) adapting a belief on the underlying model based on recently observed phenomenon dynamics and 2) correspondingly optimizing the next sensing locations. While exactly modeling stochastic real-world phenomena remains a significant challenge, this work deals mainly with modeling the underlying covariance structure. The underlying covariance structure directly corresponds to information metrics such as entropy, required for evaluating the informativeness or representativeness of sensor readings across a set of locations [9, 13, 30]. Gaussian processes (GP) have emerged as a favored choice for this specific modeling goal primarily because of their nonparametric nature [14, 20, 33, 42].