LIFT: Localization-Informed Flight Trajectories for UAV Resilience against GPS Loss

Summary

Autonomous vehicles?such as autonomous cars and drones?require highly accurate localization to operate safely, yet GPS alone often cannot provide the needed precision. Furthermore, many real-world applications?such as disaster response?often require localization in GPS-denied environments. Cooperative localization offers a promising alternative to GPS: Connected vehicles share information to localize more accurately than any one vehicle can. The accuracy of this localization depends on how they move and the sensing geometry they create. These challenges motivate new approaches that integrate localization, sensing, and planning, which aligns closely with priorities from national agencies?such as the U.S. Department of Transportation?that are exploring sensing-based PNT solutions. Researchers propose Localization-Informed Flight Trajectory (LIFT) planning to enable drones and other autonomous vehicles the ability to achieve precise and reliable localization across diverse environments, from dense urban areas to remote rural regions. This capability will improve resilience against GPS loss, which is particularly a concern in high-latitude locations (such as northern Minnesota) during geomagnetic storms. LIFT leverages sensing-enabled localization, where cooperative autonomous vehicles estimate their position relative to mobile or stationary anchors using sensor measurements (e.g., range and line-of-sight) instead of relying on GPS. Such sensing-based localization is lightweight and power-efficient, but its performance is highly sensitive to the geometric configuration of the autonomous vehicles and the anchors. This vehicle-anchor geometry directly affects localization accuracy, which in turn affects trajectory planning. Conversely, the planned trajectories influence future sensing geometry, creating a closed feedback loop among motion, sensing, and localization. This project develops LIFT, a unified framework that jointly reasoning over localization, sensing, and planning to generate UAV trajectories that maintain high localization accuracy. Researchers will introduce localization-informed constraints, develop real-time trajectory optimization algorithms, and validate the proposed LIFT framework experimentally in realistic GPS-denied environments.