Can Machine Learning Advance Beyond Visual Line of Sight Operations?
Iris Automation is a safety avionics technology company pioneering Detect-and-Avoid systems and aviation policy services that enable customers to build scalable Beyond Visual Line of Sight operations for commercial drones. The cutting edge software solution uses machine learning and computer vision to continually fine tune its capabilities on a low size, weight, and power hardware module that integrates easily onboard Unmanned Aircraft Systems allowing them to fly safely at long distances and without human intervention.
Jon Damush is CEO of Iris Automation. Jon has over 30 years of extensive aviation technology experience and executive leadership, building upon his technical background in engineering, software development and systems integration. Most recently he led new business ventures at Boeing NeXT. Before that he was Chief Growth Officer at Insitu, Inc., a Boeing subsidiary, developing novel aircraft and UAS innovation including detect-and-avoid. He was also a Boeing executive liaison and board observer to SkyGrid, LLC, a joint venture between Boeing and SparkCognition.
James Howard is a mechatronics engineer, roboticist and entrepreneur. James worked at Boeing and Insitu Pacific on computer vision research for drones such as the Scaneagle. At Spire Global, he helped build and launch their first four commercial satellites, seeing first hand the insides of a successful, highly technical aerospace startup. Additionally, in his first year of university he co-founded AniRevo Events as Director of Sales and has helped it grow successfully to a 14,000+ attendee annual event that is expanding globally.
In this edition of the Drone Radio Show, Jon and James talk about Iris Automation, the company’s innovative Casia detect and avoid technology, and how machine learning can help make the skies safer for UAS operations.
