Jul 22 2015
Drone Aviation Holding Corp., manufacturer of lighter-than-air aerostats and tethered drones, today announced that it has entered into an agreement to acquire exclusive commercial software licenses for the "GUST" autopilot system from Adaptive Flight, Inc. Through the purchase of the assets of privately held Adaptive Flight Inc. (AFI), DAC is assuming the transferable licenses from the Georgia Tech Research Corporation which include flight simulation tools and fault tolerant flight control algorithms.
In addition, DAC will acquire AFI's dedicated flight computer and additional related hardware and airframes.
Dr. Eric N. Johnson, co-founder of AFI and the Lockheed Martin Associate Professor of Avionics Integration at Georgia Tech's School of Aerospace Engineering, stated, "GUST is the basis of the unmanned aircraft systems flight research capability developed to perform research at Georgia Tech for a variety of sponsors since 2001 which has also been utilized by AFI since 2007 for commercial unmanned systems. I look forward to working with DAC on the continued commercialization of this critical enabling technology and realizing the many benefits that will result from the fusion of advanced control systems and vision-based technologies for unmanned vehicles by enhancing reliability and enabling new missions."
Jay Nussbaum, Chairman of Drone Aviation, added, "The GUST autopilot will deliver immediate value to DAC by enhancing our WATT tethered drones and expanding our market to include autonomous vehicles across a spectrum of defense-related and civilian products. This technology adds valuable intellectual property that would take years and considerable investment to reproduce and enables our long-term growth plans vital to our remaining at the forefront of unmanned vehicle development."
AFI's assets include flight control technologies for both single and multi-rotor Vertical Take-Off and Landing (VTOL) and Fixed-Wing (FW) UAVs. In addition to the GUST simulation tool, AFI's technology includes an adaptive guidance and flight control architecture which forms a complete autopilot with vision-based estimation and control capabilities. This autonomous autopilot technology was recently demonstrated at the annual American Helicopter Society (AHS) International MAV Challenge in Virginia Beach, VA. where the Georgia Tech School of Aerospace Engineering entry took first place successfully demonstrating their drone's ability to take off from a helipad and using no external aides, autonomously find a target in a search area and return to its original launch location.