| Mobile aquatic, aerial, and terrestrial robots open up rich opportunities for environmental monitoring. Sensors mounted on a robot can be moved to take measurements in multiple locations, allowing an effective spatial sampling density much higher than the number of robots. In order to most effectively exploit mobile robots in this manner, path planning methods which consider the usefulness of measurements are needed. This thesis studies and develops discrete planning algorithms for optimal usage of mobile robots in environmental monitoring applications. Specifically, we address cases where a probabilistic model (e.g., a Gaussian process) is used to predict a scalar field. In this context, the usefulness of a set of measurements collected by a robot or team of robots can be quantified as the expected reduction in entropy or mean squared error, providing a well defined objective function for the planner. We present path planning approaches which take advantage of the characteristics of these objective functions to efficiently plan optimal or near optimal paths for one or more robots.;This thesis makes the following contributions. First, we present extensions to a submodular orienteering algorithm which increases its usefulness for environmental monitoring applications. Specifically, we show how to handle temporally changing fields, and how to efficiently incorporate sensors which take measurements while the robot is moving. Second, we present a branch and bound algorithm which adapts an upper bound from feature selection literature to efficiently find the optimal solution to an informative path planning problem. Finally, we provide results from tests of the algorithms on real robotic problems, including ocean monitoring using underwater gliders, and lake monitoring using an autonomous surface vehicle. |
