Design and Validation of Dynamic Planning Algorithms to Enable Heterogeneous Airspace Operations and UAM

NASA has a bold new vision for future civil aviation ldquo;where anyone can safely fly anytime and anywhere with high confidence in a fraction of the time it takes today.rdquo; This includes Unmanned Aircraft System Traffic Management (UTM) which involves low altitude UAS operations in airspace not routinely controlled by the FAA and urban air mobility (UAM) which will involve a of a mix of autonomous, remotely piloted, and piloted air vehicles operating over populated urban areas. To enable the high-density mixed-use operations envisioned under UTM and UAM, a completely different approach to airspace management will be required. New planning algorithms are needed that can replace the cognition and decision-making capabilities of both the human pilot and air traffic controller. The overall goal of the Phase I research effort is to design a dynamic planning algorithm that is tailored to the unique challenges posed by UTM and UAM operations. The issue of algorithm validation will be considered from the onset, with a theoretical framework for assessing analytic guarantees of safety used to reduce the number of test cases required during simulation-based validation. During Phase I, the team will: (1) define a comprehensive set of safety requirements that must be addressed during path generation, (2) develop a proof-of-concept planning algorithm that meets the requirements, (3) assess the analytic guarantees of safety, and nbsp;(4) create an initial strategy for simulation-based validation that confirms the analytic guarantees provided by the planning algorithm and efficiently demonstrates that the safety requirements are met when analytic guarantees are not available. The resulting technology will begin to address how airspace operations can be scaled to an increasingly large number of aircraft with variable performance and control characteristics, including legacy users, while remaining robust to changing environmental conditions, congestion, and traffic avoidance.