Iris Automation, a developer of onboard detect-and-avoid systems for unmanned aircraft, raised an additional $13 million in Series B funding from the investment arms of Sony and Verizon as well as numerous other venture capital firms.
Leveraging machine learning and computer vision technology, along with advancements in miniaturization and computing power, Iris’ Casia system has enabled many of the groundbreaking ‘firsts’ in beyond-visual-line-of-sight (BVLOS) flight that have taken place in the past year and a half.
The Federal Aviation Administration (FAA) and other regulatory agencies around the globe continue to build pieces of their framework to enable more complex drone operations. While the aircraft certification requirements for small unmanned aircraft systems (UAS) will have little to do with how larger passenger- or cargo-carrying eVTOLs are deemed safe, regulators’ learnings and rulings around detect-and-avoid will likely prove foundational for air taxis and urban air mobility.
Aviation safety can be divided into three distinct but interrelated sections — aircraft systems, operations and pilots. For BVLOS drone operations, the safety responsibilities typically given to pilots, such as to see and avoid obstacles and other aircraft, have to be broken down and accounted for in one of the two remaining categories, Jon Damush, CEO of Iris Automation, told eVTOL.com. He expects most of these requirements to show up in operational requirements rather than unmanned aircraft type certificates.
Damush joined Iris in early October after Boeing shuttered its innovation unit, Boeing NeXT, where he was senior director of new business ventures after leading imagery analysis startup 2d3 Sensing through its acquisition by Boeing subsidiary Insitu.
The market for onboard detect-and avoid systems is closely correlated with a platform’s payload size, Damush told eVTOL.com, with the economic value of larger systems likely to be based on their autonomous capability. Iris is working closely with numerous drone manufacturers to integrate their hardware into platforms’ designs early on and intends to expand beyond detect-and-avoid into other capabilities that leverage the same vision and processing hardware, such as GPS-denied navigation and passenger boarding recognition for air taxis.
Iris is also prototyping ground-based detect-and-avoid systems for use by smaller drones that are unlikely to use onboard detect-and-avoid, due to the associated economic and size, weight and power (SWaP) limitations associated with onboard systems as well as the type of missions those drones are likely to perform. Damush expects smaller drones, unlikely to move very far, will gain autonomy through the sanitization of a static block of airspace — something easily done by ground-based systems — rather than a moving bubble around a moving aircraft, the capability normally provided by a pilot’s eyes.
“One day drones will ubiquitously operate in our airspace making our lives safer, easier, and better, and Iris Automation is the key to unlocking the full potential of commercial operations,” said Tess Hatch, Iris board member and vice president at Bessemer Venture Partners. “Enabling drones to fly beyond visual line of sight helps expand a myriad of operations from inspecting oil pipelines and railroad tracks to agricultural farms to last mile delivery, and makes all of those operations much more efficient and less expensive.”
As the FAA transitions its drone Integration Pilot Programs (IPP) into their next phase — the BEYOND program — enabling BVLOS is a key objective of the regulatory agency, and Iris will likely be a key partner. The regulatory foundations built for BVLOS drone flight will then inform rulemaking that enables eventual passenger-carrying unmanned aircraft under development by companies such as Wisk.