Long Term Autonomy for Aerial Urban Mobility

Recently there has been a lot of interest in autonomous air vehicles operating in urban areas for a diverse set of applications such as disaster response, delivery of goods, agricultural support, and urban air mobility (UAM). While the technology for unmanned air vehicles operating day in and day out without constant human supervision is maturing steadily, much remains to be done to make these vehicles commonplace. We have identified a number of challenges that must be addressed for these vehicles to safely and efficiently conduct their tasks in the National Airspace System (NAS). Civilian applications of UAS must ensure that they can: (1) Localize robustly in cluttered and changing environments without reliance on GPS, (2) Find collision-free paths reliably in these environments, (3) React to contingencies in various cases such as engine out and lost link scenarios, and (4) Deal safely with both airborne and ground based moving objects that are harder to detect and avoid than static obstacles. We propose a combination of software algorithms and methods that simultaneously solves the navigation and obstacle detection problem, especially as it relates to operation in cluttered urban environments. More specifically, in this program we will show that it is possible for autonomous air vehicles to reliably and safely fly in long-term operations.