CISE-MSI: DP: SaTC: Dynamically Enforcing User-Oriented Geospatial Restrictions for Drone Fly-Overs

Texas A&M University Corpus Christi, Corpus Christi TX

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Unmanned aircraft systems, a.k.a. drones, have recently become quite popular for commercial and recreational purposes. However, no effective solutions exist to restrict drones from flying over sensitive spaces such as homes, schools, hospitals, temporally-restricted areas, etc., either as final destinations or while in-transit. This introduces serious concerns related to: (i) privacy, e.g., drones taking pictures and/or record video without explicit consent; (ii) cyber-security, e.g., drones used as platforms for launching cyberattacks; (iii) safety, e.g., drone collisions that may result in human affectations and financial costs. To address these concerns, this project is developing No-Fly-Zone, an open-source framework that regulates drone fly-overs by providing the means to: (i) identify and delimit sensitive physical spaces; (ii) specify and enforce restrictions on drone flights; (iii) calculate flight plans for drones passing over sensitive spaces; (iv) limit airspace drone occupancy by orchestrating flight plans. Ultimately, No-Fly-Zone involves a cutting-edge research agenda that has an impact across educational, public, and commercial interests, and informs governing agencies on drone operations in the National Airspace System. To achieve the goals just mentioned, No-Fly-Zone introduces novel methodologies for: (i) modeling and delimiting protection zones over sensitive spaces by leveraging Light Detection and Ranging (lidar) and Interferometric Synthetic Aperture Radar (InSAR); (ii) understanding and writing so-called fly-over policies by leveraging Attributes and Space-Sensitive Access Control; (iii) storing, retrieving, and evaluating fly-over policies by leveraging a cloud architecture based on Distributed Hash Tables; (iv) developing navigation plans for drones by leveraging Multi-Criteria Decision Making (MCDM) and Pareto Front (PF) to model power consumption, payload capabilities, environmental parameters, protected zones, and fly-over policies; (v) orchestrating navigation plans by means of safety bubbles, which leverage Sense and Avoid (SAA) and Beyond Visual Line-of-Sight (BVLOS) to model risks based on weather conditions, occupancy limits, population density, etc. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.