Abstract

The promulgation of unmanned aircraft systems (UAS) as a ubiquitous platform has generated both aviation opportunities and challenges, particularly when it comes to integration into the national airspace (NAS). Likewise, increasingly larger autonomous aircraft in the form of advanced air mobility (AAM) systems will also require unique solutions. Each will require new systems and approaches for integration into the NAS and require navigation solutions as part of the unmanned traffic management (UTM) network development and implementation. In particular, the challenges of sensing hazards include non-traditional risks from environmental parameters such as high spatial turbulence gradients, high turbulence magnitudes, and degraded position references, all of which complicate the traditional approach. This is particularly true in urban environments where the presence of buildings produces not only obstacles but also complex, unsteady flow patterns. Local wind-field sensing onboard AAM is often approached via combining relative sensing and inertial reference measurements. Swarming and mesh network topologies are attractive for integrating additional sensing platforms into this measurement challenge. In addition, the threat of midair collisions between unmanned and manned aircraft in the NAS represents an unknown risk of integrating operations between disparate airspace users. This will be a vexing issue for the integration of urban air mobility (UAM) solutions as part of UTM development and implementation. This presentation discusses integration approaches and field tests for utilizing tools such as Artificial Intelligence (AI) and Machine Learning (ML) to provide real-time data to inform the UTM and enable AAM operations by addressing emerging needs in real-time operations. This includes use of AI/ML to improve the safety of low altitude aircraft operations through the integration of real-time observations from autonomous systems with numerical weather prediction and flight management and safety systems, improving the resolution and accuracy of comprehensive environmental and hazard estimation, which is critical to improving safety and operational efficiency.

Speaker

Dr. Jamey Jacob

Jamey Jacob is the Executive Director of the Oklahoma Aerospace Institute for Research and Education, Williams Chair in Energy Technology at OSU Tulsa, and Regents Professor of Aerospace Engineering in the School of Mechanical and Aerospace Engineering at Oklahoma State University. He has approximately $50M in funded research projects as PI and Co-PI from government and industry including AFOSR, AFRL, DOD, DOE, DARPA, FAA, NASA, NSF, SOCOM, USN, Boeing, General Electric, Lockheed-Martin, Northrop-Grumman, and Toyota, among others. He is currently PI on the EDA funded LaunchPad and UAS Flight Corridor programs, OSU lead for the Tulsa Hub for Ethical and Trustworthy Autonomy Tech Hub, the NASA University Leadership Initiative WINDMAP to develop aviation weather solutions for advanced aerial mobility applications, including drones and urban air taxis, and a NIST effort on the Development of Test Certification Standards for AAM Platforms. He also leads the Counter-UAS Center of Excellence.

He received his B.S. in Aerospace Engineering from the University of Oklahoma in 1990 and his M.S and Ph.D. in Mechanical Engineering from the University of California at Berkeley in 1992 and 1995, respectively. He was a National Research Council Summer Faculty Fellow in the Air Force Research Laboratory. He received the SAE Ralph Teetor Award, the Lockheed Martin Teaching Award, and the OSU Regents Distinguished Teaching Award, among other teaching and mentoring awards. He is a native Oklahoman and dedicates much of his efforts to education and STEM workforce development. He is a Part 107 UAS pilot with approximately 500 hours operating various platforms.