Lane-Based Unmanned Aircraft Systems Traffic Management

The age of Advanced Air Mobility (AAM) is upon us, and in ushering new ways to connect and travel, this wave of technology has been compared to GPS and cloud computing. However, new technologies like AAM require tools to build, expand, and understand the capabilities. This book describes an effective and efficient, complete solution to the large-scale, unmanned aircraft systems (UAS) traffic management problem. The authors present a detailed perspective and solutions to some of the major problems involved in coordinating thousands of autonomous vehicles including: virtual highway (lane) creation, strategic deconfliction of flights, dynamic deconfliction, UAS agent behavior learning, anomalous trajectory detection and classification, as well as a set of simulation results for a variety of scenarios (city package delivery, earthquake supply delivery, coalition force coordination through the lane reservation system, etc.).

Shows how to create and manage a structured urban airspace Provides strategic deconfliction Enables practical application anywhere

Auteur
David Sacharny received a BS in Electrical Engineering from California Polytechnic State University, San Luis Obispo in 2010. He is a PhD candidate in computing and robotics at the University of Utah, and a managing member of GeoRq LLC. Previously, he worked as a lead engineer and developer for L3Harris (formerly L3 Communications) on a range of programs, including Apache MUMT-X, Gray Eagle UAS, and Broad Area Maritime Surveillance (MQ-4C Triton UAS). His research interests concern geospatial intelligence, large-scale UAS traffic management, and artificial intelligence. In 2020, his research spinoff GeoRq LLC signed a space act agreement with NASA and was one of a few challengers in the Advanced Air Mobility National Campaign Developmental Test (NC-1) airspace simulations. He is listed on several patents related to big-data, internet-of-things, and autonomous vehicles.

Thomas C. Henderson received his BS in Math with Honors from Louisiana State University in 1973 and his PhD in Computer Science from the University of Texas at Austin in 1979. He is currently a full Professor in the School of Computing at the University of Utah. He has been at Utah since 1982, and was a visiting professor at DLR in Germany in 1980, and at INRIA in France in 1981 and 1987, and at the University of Karlsruhe, Germany in 2003 and 2011, and was a Program Director at the National Science Foundation in 2010. Prof. Henderson was chairman of the Department of Computer Science at Utah from 1991-1997, and was the founding Director of the School of Computing from 2000-2003. Prof. Henderson is the author of Discrete Relaxation Techniques (University of Oxford Press, 1990), Computational Sensor Networks (Springer, 2009), Analysis of Engineering Drawings and Raster Map Images (Springer, 2014) and editor of Traditional and Non-Traditional Robotic Sensors (Springer-Verlag, 1990); he has served as Co-Editor-in-Chief of the Journal of Robotics and Autonomous Systems and as an Associate Editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Robotics and Automation. His research interests include autonomous cognitive systems, robotics and computer vision, and his ultimate goal is to help realize functional androids. Prof. Henderson is a Fellow of the IEEE, and received the Governor's Medal for Science and Technology in 2000. He enjoys good dinners with friends, reading, playing basketball and hiking.

Contenu

Overview of UAS Economic Impact and Current State of Affairs.- Status of FAA-NASA UTM Approach.- Overview of Lane-Based Method.- Lane Network Creation and Spatial Network Measures.- Strategic Deconfliction (making sure flights are safe!).- Monitoring UAS Flights and Rogue Detection.- Comparison of FAA-NASA vs Lane-Based Method.- Contingency Handling (when things go wrong).- Agent Based Modeling and Simulation of UTM-UAS Systems.- UAS Supply Delivery in Earthquake Scenario.- UAS Coalition Forces Coordination Scenario.