The Automation of Uncrewed Aircraft Systems Traffic Management Calibration Based on Experimental Platform Data

Abstract

Many countries are developing an Urban Air Mobility (UAM) capability defining an Uncrewed Aircraft Systems (UAS) Traffic Management (UTM) architecture to allow safe UAS services in urban environments (e.g., delivery, inspection, air taxis, etc.). The main considerations are air worthiness, operator certification, air traffic management, C2 Link, detect and avoid (DAA), safety management, and security. In addition, if thousands of simultaneous UAS flights are to be achieved, it is not possible for them to be controlled individually by human operators. This makes it necessary to have a rigorous and safe automation methodology to handle such a number of flights. A lane-based airspace structure has been proposed which reduces the complexity of strategic deconfliction by providing UAS agents with a set of pre-defined airway corridors called lanes [1], [2]. This yields collateral benefits including UAS information privacy, robust contingency handling exploiting the lane structure, as well as improved observability and control of the air space. A robust set of UTM parameters and policies must be determined based on the performance characteristics of the deployed UAS platforms, and a methodology which constitutes a first step toward this end is proposed and demonstrated here. In order to realize this approach, a set of initial experiments have been performed to determine the constraints imposed by the UTM on UAS platform capabilities and vice versa. Initial implementation parameters and policies are defined. The major contribution here is a methodology to calibrate UTM safety parameters (e.g., headway, platform speed) in terms of specific platform models' operational characteristics. That is, UTM parameters are a function of platform and not some arbitrarily imposed values. Safety uncertainty is then characterized by the calibration method.