FADS: A framework for autonomous drone safety using temporal logic-based trajectory planning

In this work, we present an integrated Framework for Autonomous Drone Safety (FADS). The demand for safe and efficient mobility of people and goods is growing rapidly, in line with the growth in population in US urban centers. In response, new technologies to meet these urban mobility demands are also rapidly maturing in preparation for future full-scale deployment. As surface congestion increases and the technology surrounding unmanned aerial systems (UAS) matures, more people are looking to the urban airspace and Urban Air Mobility (UAM) as a piece of the puzzle to promote mobility in cities. However, the lack of coordination between UAS stakeholders, federal UAS safety regulations, and researchers developing UAS algorithms continues to be a critical barrier to widespread UAS adoption. FADS takes into account federal UAS safety requirements, UAM challenge scenarios, contingency events, as well as stakeholder-specific operational requirements. FADS formalizes these requirements, through Signal Temporal Logic (STL) representations, and a trajectory planning optimization for multi-rotor UAS fleets guarantees robust and continuous-time satisfaction of the requirements and mission objectives. The intuitive FADS user interface makes it easy to plan missions in a variety of environments; we demonstrate this through several rural and urban environment-based case studies. FADS holistically integrates high-level stakeholder objectives with low-level trajectory planning; combined with a user-friendly interface, FADS reduces the complexity of stakeholder coordination within the UAM context.