Dr. Jonathan Rogers is an associate professor in the Guggenheim School of Aerospace Engineering. Prior to his appointment in the School of Aerospace Engineering, Dr. Rogers served as a faculty member in the School of Mechanical Engineering at Georgia Tech and the Department of Aerospace Engineering at Texas A&M University. Dr. Rogers is director of the Aerial Robotics and Experimental Autonomy Lab (AREAL) at Georgia Tech where his group conducts research in applied dynamics, controls, robotics, and autonomy. Through a combination of theoretical and applied research, Dr. Rogers has developed groundbreaking new technologies in a variety of areas from rotorcraft and smart weapons to stochastic optimal control. He is the recipient of the NSF CAREER Award and the Lockheed Martin Inspirational Young Faculty Award. His work has been featured by MIT Technology Review, Engadget, BBC news, and IEEE Spectrum.
Areas of Expertise (4)
Systems Design and Optimization
Selected Accomplishments (2)
NSF CAREER Award
NSF CAREER Award (2016)
Lockheed Martin Associate Professor of Avionics Integration
Lockheed Martin Associate Professor of Avionics Integration (2019)
Georgia Institute Technology: Ph.D., Aerospace Engineering 2009
Georgia Institute Technology: M.S., Aerospace Engineering 2007
Georgetown University: B.S., Physics 2006
Selected Articles (1)
2019 Cooperative transportation of payloads by multiple unmanned air vehicles has received increasing interest due to unique operational advantages. These include the portability of the individual vehicles and the scalability of the lifting strategy in the presence of differing payloads. By rigidly attaching a set of unmanned air vehicles to a payload, the control effort required to transport the payload is divided across the vehicles. In the presence of uncertainty about a payload’s mass and inertial characteristics, there is no inherent flightworthiness guarantee for a specific connected unmanned air vehicle configuration. This Paper describes a method for determining on-ground flightworthiness of the unmanned air vehicle-payload system while making minimal assumptions about inertial properties of the payload or the attachment configuration of the unmanned air vehicles. The probabilistic model itself is initialized and updated (built) by the algorithm. Within the model, the vehicles are positioned. Building upon prior theoretical developments, this Paper explores the differing sources of error in the estimates and experimentally validates the algorithm through a series of tests using prototype modular vehicles. Overall, simulation and test results highlight the dominant performance factors and demonstrate the feasibility of the approach for a range of payload geometries.