UAV swarm search guided by uncertainty in GNSS-denied maritime areas
Localization-Uncertainty-Aware UAV Swarm Search for GNSS-Denied Maritime Surveillance
Robotics
Summary
Finding targets at sea without GPS is challenging for drone swarms because they rely on each other to figure out where they are. The authors created a method that tracks how location errors grow as drones share signals and adjusts their movements to keep them connected, avoid crashes, and cover the search area well. Their approach uses math to estimate uncertainty and plans paths so drones keep safe margins and maintain good coverage. They tested it with simulations and real-world drone contests, showing reliable search completion.
What this means in practice
- •For maritime surveillance teams: Guide drone swarms to search maritime areas without GPS by managing location uncertainty for safer, more effective coverage.
- •For emergency response coordinators: Coordinate GPS-denied drone operations in disaster zones with uncertain positioning to maintain connectivity and avoid collisions.
Authors
Siyuan Yang, Junning Lyu, Xiaoyu He, Lakmal Seneviratne, Shaoming He
Abstract
This paper studies UAV swarm search in GNSS-denied maritime environments, where inter-UAV ranging drives cooperative localization and uncertainty propagates along the multi-hop reference chain, affecting connectivity, collision avoidance, and search effectiveness. We propose an uncertainty-aware control barrier function (CBF) framework approximating cascading localization covariance through Fisher information matrices. First, the cascading covariance model captures how ranging noise and neighbor uncertainty accumulate along the localization chain. Second, uncertainty-tightened CBFs incorporate the covariance model and its rate into margins for connectivity maintenance and collision avoidance, with conditional invariance and graceful-degradation bounds. Third, a hierarchical Voronoi-target strategy with formation keeping supports efficient forward-looking coverage. The distributed controller is validated with a standard unbiased extended Kalman filter in the loop. The framework is demonstrated on several polygon shapes and swarm sizes from 6 to 30 sUAVs. An estimator-in-the-loop run completes the search in 302.5 s, 20 Monte Carlo deployments confirm repeatable completion with all local QPs feasible and containment in at least 98.97% of UAV-frames, and six MBZIRC runs provide estimator-in-the-loop evidence. The source code is publicly available.