48 Technology focus | Uncrewed Aircraft System Traffic Management location, altitude and speed of UAS) via the UTM system. Connecting the UTM simulator to the UTM system helped to verify the methods and procedures for avoiding mid-air collisions when a drone suddenly changes its flight route and approaches the proximity of other drones. AI algos The growth in Urban Air Mobility (UAM) and UAV operation is expected to see a significant growth in air traffic, and AI algorithms are viewed as a way to provide more automation. However, these can be difficult to certify, and they provide a deterministic system that can operate reliably and be approved by regulators. Various forms of AI are being developed to support the UTM. One technique is based on aerial photos and geodata to automatically identify no-fly zones. The AI framework identifies appropriate landmarks in aerial photos that account for no-fly zones, such as over airports, federal waterways or wind turbines, in an automated process. Machine vision and learning methods are able to reliably detect structures and patterns, and classify them correctly. Information from aerial photos is supplemented by geoinformation from various sources and its heterogeneity is a major challenge. The goal is to visually process all data for the user in an easily understandable way in order to provide a transparent view, and this data helps with immediate decision-making. To accomplish this, a platform is being developed with AI-based computervision algorithms to automatically collect, process, virtually integrate and, ultimately, visually provide flight-relevant geoinformation. Another approach to path planning problems with separation constraints for ATM also uses AI. MuZero is a deep reinforcement-learning algorithm that can be used for path-planning problems in dynamic air traffic environments. It uses a sequential, trajectory allocation approach that would act on a first come, first served basis for both online planning and moving time horizon problems. Initial results show that agents can learn to mitigate collisions when trained with the obstacle avoidance framework, based on the MuZero algorithm, without requiring any knowledge of the domain rules. Acknowledgements With thanks to Kraetti Epperson of Vigilant Aerospace Systems, Brent Klavon at ANRA and Harada Kenya of Jaxa. August/September 2024 | Uncrewed Systems Technology FRANCE Thales Aerospace - www.thales.com ISRAEL Airwayz - www.airwayz.co POLAND Drone Radar +48 511 230 660 www.droneradar.eu UK Altitude Angel +44 118 391 3503 www.altitudeangel.com USA ANRA Technologies www.anratechnologies.com +1 703 239 3209 Vigilant Aerospace +1 405 445 7224 www.vigilantaerospace.com Some examples of third-party UTM suppliers A UTM test in Japan (Image courtesy of JAXA)
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