Dr. Shawn Keshmiri
- Research Lead
- Spahr Professor
Contact Info
2117A Learned Hall
Personal Links
Biography —
Dr. Shawn Keshmiri is an Associate Professor in the Department of Aerospace Engineering, University of Kansas, USA with expertise in the Guidance, Navigation and Control, and Flight Test Engineering.
Much of his work has been on improving the understanding of unsteady and nonlinear aerodynamics and developing Adaptive Control. In the Adaptive Control arena, he has developed nonlinear robust predictive control. He has explored navigation of scalable number of unmanned aerial systems in unstructured environments. Keshmiri’s research has been supported by the National Science Foundation (NSF), NASA, Lockheed Martin, and Microsoft Research, and other industrial partners. Dr. Keshmiri leads UAS flight test activities at the KU Flight Research Lab, University of Kansas and led several UAS flight test deployments in Antarctica, Greenland, and U.S. continent. Dr. Keshmiri’s team successfully developed guidance, navigation, and control algorithms for multi-agent unmanned aerial systems (UASs) with high speeds and high inertias and performed validation and verification flight testes between two large collaborative UASs. Dr. Keshmiri received University of Kansas H.O.P.E. (Honor for the Outstanding Progressive Educator) award in 2018 and became John E. and Winifred Sharp Teaching Professor in 2017. He is an Associate Fellow of the American Institute of Aeronautical and Astronautics since 2015.
Education —
B.S., Shiraz University
M.S., CSULA University
Ph.D., University of Kansas
Research —
My key research objective is to understand and establish necessary foundations in the flight control to enable cognitive and collaborative unmanned aircraft systems (UASs). I worked closely with my graduate students to advance the state-of-the art in flight control of multi-agent UASs and open a new chapter in the Flight Control that I call “evolving and learning autopilots”.
In my research I tackle the swarm problem from two different perspectives: (1) Developing advanced guidance, navigation, and control (GNC) algorithms for large UASs with high speeds and high inertia (2) To develop an agent based system with agents that observe the behavior of an artificial swarm of autonomous robots and aggregates agent information to predict future states of the swarm.
am solving the complexity of the swarm problem through increasing autonomy of UASs. Machine learning and artificial intelligent research has led to many tangible results and recent developments in cognitive control and decision making. This leaves little doubt that we are entering a new era where autonomous and intelligent robots will bring dramatic changes to Aerospace Engineering and especially to the science of Flight Control. My research aims to take advantage of high-performance computing platforms and the state-of-the art machine learning and verification algorithms to develop a new intelligent, adaptable, and certifiable flight control system with learning capabilities.