Issued patent
Airborne Machine Learning Estimates for Local Winds and Kinematics
An issued NASA patent from the Technology Transfer catalog.
About this technology
Overview
Future Unmanned Aerial Systems (UAS) and air taxis will require advanced onboard autonomy to operate safely within complex and dynamic urban environments. Urban landscapes are dynamic and constantly evolving. In addition to multi-directional, intense, and seemingly unpredictable winds often created in urban canyons, an exact knowledge of current building sizes, shapes, and positions is also often unavailable for real-time navigation. NASA Ames has developed a novel system, MAESTRO: MAchine learning ESTimations for uRban Operations, which not only improves the flight safety of UAS and air taxis in complex dynamic environments but also allows them to make smart and rapid on-board estimations of the local surrounding winds and vehicle kinematics using commercial off-the-shelf (COTS) sensors and advanced onboard computing.