Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling data is typically sparse and limited. The challenges require that the sampling method must be informative and efficient enough to catch up with the environmental dynamics. In this paper we present a planning and learning method that enables a sampling robot to perform persistent monitoring tasks by learning and refining a spatiotemporal environmental model. Our environmental sampling framework consists of two components: to maximize the information collected, we propose an informative planning component that efficiently generates sampling waypoints that contain the maximal information; To alleviate the computational bottleneck caused by large-scale data accumulated, we develop a component based on a sparse Gaussian Process whose hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. We validate our method with both simulations running on real ocean data and field trials with an ASV in a lake environment. Our experiments show that the proposed framework is both accurate and efficient in learning the spatiotemporal environmental model
In many sensing applications we must continuously gather information to provide a good estimate of the state of the environment at every point in time. A robot may tour an environment, gathering information every hour. In a wireless sensor network, these tours correspond to packets being transmitted. In these settings, we are often faced with resource restrictions, like energy constraints. The users issue queries with certain expectations on the answer quality. Thus, we must optimize the tours to ensure the satisfaction of the user constraints, while at the same time minimize the cost of the query plan. For a single timestep, this optimization problem is NP-hard, but recent approximation algorithms with theoretical guarantees provide good solutions. In this paper, we present a new efficient algorithm, exploiting dynamic programming and submodularity of the information collected, that efficiently plans data collection tours for an entire (finite) horizon. Our algorithm can use any single step procedure as a black box, and, based on its properties, provides strong theoretical guarantees for the solution. We also provide an extensive empirical analysis demonstrating the benefits of nonmyopic planning in two real world sensing applications. We introduce a graph-based informative path planning algorithm for a mobile robot which explicitly handles time. The objective function must be submodular in the samples taken by the robot, and the samples obtained are allowed to depend on the time at which the robot visits each location. Using a submodular objective function allows our algorithm to handle problems with diminishing returns, e.g. the case when taking a sample provides less utility when other nearby points have already been sampled. We give a formal description of this framework wherein an objective function that maps the path of the robot to the set of samples taken is defined. We also show how this framework can handle the case in which the robot takes samples along the edges of the graph. A proof of the approximation guarantee for the algorithm is given. Finally, quantitative results are shown for three problems: one simple example with a known Gaussian process model, one simulated example for an underwater robot planning problem using data from a well-known ocean modeling system, and one field experiment using an autonomous surface vehicle (ASV) measuring wireless signal strength on a lake. 