Inference and Planning for Aggressive Autonomous Flight
Getting a small unmanned aircraft to fly aggressively and autonomously through an unknown, cluttered environment creates substantial challenges for the vehicle's navigation and control. Without a prior map, the vehicle has to detect obstacles and avoid them, often on the basis of little sensor data, and make rapid decisions about how to move around given an uncertain and incomplete model of the world and the vehicle¹s position. I will discuss some recent results from my group in developing approximate inference and planning algorithms that have enabled fast and aggressive autonomous motion for unmanned vehicles.
Nicholas Roy is an Associate Professor in the Department of Aeronautics & Astronautics at the Massachusetts Institute of Technology and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He received his Ph. D. in Robotics from Carnegie Mellon University in 2003. His research interests include unmanned aerial vehicles, autonomous systems, human-computer interaction, decision-making under uncertainty and machine learning. He spent two years at Google [x] as the founder of Project Wing.