Title: Robot Information Gathering for Dynamic Systems in Wildfire Scenarios

Abstract:
The monitoring of complex dynamic systems, such as those encountered in disaster response, search and rescue, wildlife conservation, and environmental monitoring, presents the fundamental challenge of how to track efficiently with limited resources and partial observability. This thesis presents algorithms and techniques for robotic information gathering in dynamic and unstructured environments. The core research focuses on two key areas: modeling and updating the belief of a dynamic system, and efficiently creating long-term plans based on a dynamic belief to maximize information. We document our approach to creating a full robotic system that incorporates these algorithms for real-life wildland fire safety monitoring. Specifically, we detail the development of a hexacopter unmanned aerial system to track the position of wildland fire crew. To enable effective crew localization, we developed a perception pipeline trained on our real-world thermal imagery dataset. Our closed-loop system enables the robot to perceive, update its belief, create a plan, and execute that plan to gather new observations about the world. We demonstrate the effectiveness of our approach through simulation and real-world tests. Finally, we discuss future directions for our robot to perform robustly in more challenging environments.