A Beacon Based Solution for Autonomous UUVs GNSS-Denied Stealthy Navigation

TL;DR

The paper addresses stealthy GNSS-denied underwater navigation for a fleet of UUVs by introducing a beacon-based localization framework where acoustic beacons deployed by aerial or surface platforms provide synthetic landmarks guiding UUVs along an optimized path. A hierarchical HTN planning approach is used to synthesize a navigation plan from beacon positions, implemented as QGIS plugins (Beacon deployment and TraceQGIS) with a closed-loop execution that supports replanning under uncertainty. A Constrained Lloyd's algorithm is proposed to optimally place beacons by minimizing the deviation of beacon-coverage volumes, producing equal-volume Voronoi cells whose centroids define deployment points. The work demonstrates the integration of acoustic beacons, hierarchical planning, and geospatial tooling to enable robust, GNSS-denied navigation, while outlining future enhancements for fleet coordination, real-time trilateration, and temporal HTN capabilities. $ \min \frac{1}{N} \sum_{n \le N} \left| V_n - \frac{V_{tot}}{N} \right| $ will guide beacon placement in the deployment optimization. $

Abstract

Autonomous Unmanned Underwater Vehicles (UUVs) enable military and civilian covert operations in coastal areas without relying on support vessels or Global Navigation Satellite Systems (GNSS). Such operations are critical when surface access is not possible and stealthy navigation is required in restricted environments such as protected zones or dangerous areas under access ban. GNSS denied navigation is then essential to maintaining concealment as surfacing could expose UUVs to detection. To ensure a precise fleet positioning a constellation of beacons deployed by aerial or surface drones establish a synthetic landmark network that will guide the fleet of UUVs along an optimized path from the continental shelf to the goal on the shore. These beacons either submerged or floating emit acoustic signals for UUV localisation and navigation. A hierarchical planner generates an adaptive route for the drones executing primitive actions while continuously monitoring and replanning as needed to maintain trajectory accuracy.

Figures (3)

• Figure 1: The reactive planning framework. The plan is executed in a closed-loop fashion, where each action is applied in the environment and a monitor gathers the sensors outcome and triggers a replanning episode if needed.
• Figure 2: QGIS plugin interface featuring the beacons, and the UUVs off the coast of Capo Testa, Sardinia. On the right, the planning interface, with the time cursor, and the option to focus on the entities present on the map. On the left, the layers for the mission, and the bathymetric data.
• Figure 3: A mission example executed in TraceQGIS illustrating the features of the navigation plugin.