Intelligent Information-Gathering: Using Control for Sensing and Decision-Making

Information is everywhere and evolving, which necessitates both deliberate and efficient processing to acquire a good understanding of the dynamic situation, environment, or system of interest. Intelligent agents such as autonomous mobile sensors can control the way they gather information and thereby take advantage of feedback to improve the quality of that information. This approach reflects a shift from traditional "sensing for control" notions to "control for sensing" methods for addressing information-based objectives. This thesis presents several algorithms for distributed sensing tasks in the context of a team of mobile sensing agents. Applications of these types of mobile sensor networks include target tracking, dynamic environment monitoring, and distributed classification. These methods point beyond the use of sensory data for control and toward a framework for using control to improve information-based decisions made by intelligent agents. The sequential decision-theoretic framework presented herein has relevant applications in engineered systems such as search and rescue using a robotic team, as well as potential connections to natural systems including search strategies in the human vision system