Fast, Efficient, and Robust Autonomy for Unmanned Aerial Systems
Dr. Jonathan How
Richard C. Maclaurin Professor of Aeronautics and Astronautics – Massachusetts Institute of Technology
AIAA Director – American Automatic Control Council
Abstract
Unmanned aerial systems (UAS) have a wide range of applications such as search-and-rescue, surveillance, and package delivery. However, deploying UAS in real-world scenarios involves significant challenges, including planning under uncertainty, operating in GPS-denied environments, and navigating dynamic, unknown spaces. This talk will discuss these challenges and provide three innovative solutions that enable robust and efficient autonomy for UAS. This includes our Robust Tube Model Predictive Control (RTMPC) approach that generates robust trajectories under modeling, actuation, and sensing uncertainty. By leveraging Tube-Guided Data Augmentation, our approach efficiently augments the state-action pairs of data used to train policies that are robust to previously unseen disturbances, significantly reducing the need for large experimental datasets. These strategies are implemented with an Imitation Learning framework that enhances learning efficiency and adapts to new tasks with minimal data collection required. For operations in GPS-denied environments, we introduce PUMA (Perception- and Uncertainty-aware Trajectory Planner), a novel planner that merges obstacle tracking with the exploration of unknown spaces. PUMA leverages learning-based image segmentation to create sparse, shareable maps, enabling decentralized, safe operations and consistent information sharing among agents. Finally, we present DYNUS, a framework designed for navigating dynamic, unknown environments without relying on prior assumptions about static or dynamic obstacles. DYNUS achieves fast, safe replanning through a hybrid learning and optimization-based approach, ensuring safe navigation in highly uncertain conditions. Extensive simulations and experimental results will be shown to illustrate the effectiveness of these algorithms, and the insights from those experiments will lead to a discussion of future research directions in robust autonomy for UAS.
Biography of the speaker
Jonathan P. How is the Richard C. Maclaurin Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He received a B.A.Sc. (Aerospace) from the University of Toronto in 1987, and his S.M. and Ph.D. in Aeronautics and Astronautics from MIT in 1990 and 1993, respectively, and then studied for 1.5 years at MIT as a postdoctoral associate. Prior to joining MIT in 2000, he was an assistant professor in the Department of Aeronautics and Astronautics at Stanford University.
Dr. How was the editor-in-chief of the IEEE Control Systems Magazine (2015-19), an associate editor for the AIAA Journal of Aerospace Information Systems (2012-21) and IEEE Transactions on Neural Networks and Learning Systems (2018-21). He was the Program Vice-chair (tutorials) for the 2021 Conference on Decision and Control and is the Program Chair for the American Control Conference in 2025. He was elected to the Board of Governors of the IEEE CSS for 2020-22, is a member of the IEEE CSS Executive Committee (VP Finance) (2023-24), is on the IEEE CSS Long Range Planning Committee (2022 – ), is a member of the IEEE CSS Technical Committee on Aerospace Control and the Technical Committee on Intelligent Control, was a member of the IEEE Fellows Selection committee for CSS (2021-22), and since 2021, he serves as the AIAA Director on the American Automatic Control Council. He was a member of the USAF Scientific Advisory Board (SAB) from 2014-17.
His research focuses on robust planning and learning under uncertainty with an emphasis on multiagent systems, and he was the planning and control lead for the MIT DARPA Urban Challenge team. His work has been recognized with multiple awards, including receiving the IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award for 2022, being inducted into the University of Toronto Engineering Hall of Distinction (2022), receiving the 2020 IEEE CSS Distinguished Member Award, the 2020 AIAA Intelligent Systems Award, the 2015 AeroLion Technologies Outstanding Paper Award for Unmanned Systems, the 2015 IEEE CSS Video Clip Contest, the 2011 IFAC Automatica award for best applications paper, and the 2002 Institute of Navigation Burka Award. He also received the Air Force Commander’s Public Service Award in 2017. He is a Fellow of IEEE (2018) and AIAA (2016) and was elected to the National Academy of Engineering in 2021.