• Ph.D., Aerospace Engineering, Georgia Institute of Technology, 2009
  • M.S., Aerospace Engineering, Georgia Institute of Technology, 2007
  • B.S., Physics, Georgetown University, 2006


Jonathan Rogers joined the Georgia Tech faculty in Fall 2013 as an Assistant Professor of Mechanical Engineering.  Prior to joining Georgia Tech, he was an Assistant Professor of Aerospace Engineering at Texas A&M University from 2011 to 2013.


Dr. Jonathan Rogers conducts research at the intersection of robotics, nonlinear dynamics, and control system engineering toward the overall goal of developing and improving actively-controlled autonomous vehicles.  Particular research interests include vehicle design, nonlinear control and  estimation, real-time signal processing, and sensor fusion.  Application focus areas range from autonomous ground and marine vehicles to rotorcraft and smart weapons systems.  Specific emphasis is placed on developing a fundamental understanding of theoretical principles with a focus on applying theory to solve real-world problems.  Experimental testing plays a key role in this work to characterize complex interactions that cannot be captured in simulation.

Recently, Dr. Rogers has become interested in morphing vehicle concepts that can exhibit hybrid locomotion or traverse through more than one type of media.  The inspiration for this work stems from biological systems – for instance, sea birds are extremely efficient in flight but also prove to be powerful underwater swimmers for short durations.  Novel vehicle configurations and designs are required to enable this type of operational flexibility.  The “Pterodactyl” morphing aircraft currently under development is one such vehicle that can obtain highly efficient forward flight while maintaining vertical lift capability.

The interface of guidance, navigation, and control with emerging massively parallel processing is also of great interest to Dr. Rogers.  With the recent rise of general purpose computing on graphics processing units (GPU’s), researchers in all fields are recognizing the transformative potential that underlies specialized processing hardware.  For guidance and control tasks, GPU’s can enable uncertainty propagation through massively parallel Monte Carlo simulation.  Nonlinear, non-Gaussian uncertainty quantification is a primary element of so-called stochastic model predictive control, which can enable critical trajectory optimization and obstacle avoidance tasks for robotics systems.  Recent results show that GPU-enabled stochastic model predictive control can greatly improve performance of robotic systems that are underactuated or forced to operate in complex environments.