Optimal Information Gathering in Stochastic Environments
Future autonomous systems will operate in unfamiliar areas with minimal or no human interaction for prolonged periods of time. The luxury of building prior detailed maps of these environments could be (1) prohibitive (e.g., disaster areas), (2) impractical (e.g., signal landscapes and congested downtowns), or (3) economically not viable (e.g., hospital buildings and national forests). With no human-in-the-loop before or during operation, one expects future autonomous systems to (1) possess full situational awareness and (2) gather sufficient information about their environment. These two tasks need to seamlessly integrate into the overall mission of the autonomous system.
Our research in this thrust focuses on developing theory, algorithms, and tools for autonomous systems deployed in unknown, dynamic stochastic environments to optimally gather sufficient information to successfully accomplish their mission. Our research specifically considers autonomous systems with limited sensing, computation, actuation, and communication capabilities.
Receding Horizon Trajectory Optimization
Greedy Motion Planning
Optimal Collaborative Mapping of Signals of Opportunity
Information Gathering Architectures