Organizations: Dept of Computer and Information Science, University of Konstanz, Germany · Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany · Dept of Aerospace Engineering, Indian Institute of Science, Bangalore, India
Accurate and continuous localization of Autonomous Underwater Vehicles (AUVs) in GPS-denied environments is a persistent challenge in marine robotics. In the absence of external position fixes, AUVs rely on inertial dead-reckoning, which accumulates unbounded drift due to sensor bias and noise. This paper presents BIND-USBL, a cooperative localization framework in which a fleet of Autonomous Surface Vessels (ASVs) equipped with Ultra-Short Baseline (USBL) acoustic positioning systems provides intermittent fixes to bound AUV dead-reckoning error. The key insight is that long-duration navigation failure is driven not by the accuracy of individual USBL measurements, but by the temporal sparsity and geometric availability of those fixes. BIND-USBL combines a multi-ASV formation model linking survey scale and anchor placement to acoustic coverage, a conflict-graph-based TDMA uplink scheduler for shared-channel servicing, and delayed fusion of received USBL updates with drift-prone dead reckoning. The framework is evaluated in the HoloOcean simulator using heterogeneous ASV-AUV teams executing lawnmower coverage missions. The results show that localization performance is shaped by the interaction of survey scale, acoustic coverage, team composition, and ASV-formation geometry. Further, the spatial-reuse scheduler improves per-AUV fix delivery rate without violating the no-collision constraint, while maintaining low end-to-end fix latency.
Figures & tables
Fig. 1: Multi-AUV boustrophedon coverage at L=60 m. The single ASV (star) is station-kept at the survey centroid; all four lawnmower strips fall within its HF uplink range = 100 m.
Fig. 2: Trajectory overview: L=140 m, 3 AUVs, 1 ASV (star, α0=0∘ ). The dashed blue circle marks the HF uplink range, and the dotted orange circle marks the MF downlink range. The upper lawnmower strip lies almost entirely outside the HF footprint.
Fig. 3: Fleet metrics for the single-ASV failure case ( L=140 m, 3 AUVs, 1 ASV, α0=0∘ ). Left: cross-track error over time; AUV 0 diverges unboundedly while AUV 1 –AUV 2 are bounded. Right: cumulative fix count; AUV 0 ’s count flatlines after the first few seconds, characteristic of coverage-limited starvation.
Fig. 4: Per-AUV CTE, fix count, and HF coverage for L=140 m, 3 AUVs, 2 ASVs across formation angles α0∈{0∘,30∘,60∘,90∘} (single representative run per angle). At α0=0∘ the two-ASV pair is aligned along the x -axis, leaving y -extreme AUVs unserved. A 30∘ rotation yields an 8× CTE reduction for AUV 0 with no change in hardware.
Config
AUV
Fixes
Cov. (%)
CTE (m)
Dist. (m)
1 ASV, 0∘
AUV 0
19
9
10.02
192
AUV 1
149
70
1.79
190
AUV 2
123
58
2.52
182
3 ASVs, 0∘
AUV 0
92
79
0.56
192
AUV 1
189
100
0.35
190
AUV 2
185
100
0.35
182
TABLE I: Per-AUV metrics for the coverage-failure and recovery cases ( L=140 m, 3 AUVs, single representative run).
Fig. 5: Mean CTE (m) across all formation angles and 20 seeds. Columns: survey side lengths 60 , 100 , 140 m. Rows: ASV count; horizontal axis: AUV count. Green ≤1 m (well-localized); red >4 m (navigation failure).
Fig. 6: Distance-normalised configuration sweep ( 20 seeds, mean ±1σ ). Left column: mean position error. Right column: applied fix rate. Rows: L=60 , 100 , 140 m. Colors denote formation angle; line styles: solid =1 ASV, dashed =2 ASVs, dotted =3 ASVs. The sharp transition from solid to dotted lines at L=140 m illustrates the binary nature of the coverage threshold.
Global navigation satellite system (GNSS) denial can prevent an unmanned surface vehicle (USV) from both finding a distant vessel and maintaining a globally referenced approach. This paper presents AUSLUN (Automatic UAV Search, Localization, and USV Navigation), a fixed-hover aerial-surface system that uses a coastal unmanned aerial vehicle (UAV), which estimates its own pose through visual-inertial odometry (VIO), as a long-range sensing and navigation anchor. The central design shifts sensing motion from UAV translation to a zoom pod and closes the loop through three coupled elements: polygon-aware annular pod scanning, modality-aware bearing-range localization, and target-relative USV guidance with visual-loss recovery. The same gated recursive estimator uses laser range for the non-cooperative target and datalink range for the cooperative USV. Search-planning simulations show that the adaptive yaw bounds reduce scan time and redundant coverage relative to a matched fixed-sector scan, and GPS-referenced field data show that the gated recursive estimator outperforms non-recursive baselines in localization accuracy. An integrated maritime mission further demonstrates the complete search-to-navigation sequence, including a deliberately triggered visual-loss recovery. These results establish the feasibility and operating boundary of fixed-hover UAV assistance for stationary-target approach in coastal GNSS-denied environments. The source code and a video demonstration are publicly available at https://github.com/xirhxq/pod_search and https://youtu.be/S-5RkJs35JI.
Siyuan Yang, Zikai Jia, Hailiang Kuang +4
School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China · Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi 127788, United Arab Emirates
We present a safety-oriented framework for autonomous underwater vehicles (AUVs) that improves localization accuracy, enhances trajectory prediction, and supports efficient search operations during communication loss. Acoustic signals emitted by the AUV are detected by a network of fixed buoys, which compute Time-Difference-of-Arrival (TDOA) range-difference measurements serving as position observations. These observations are subsequently fused with a Kalman-based prediction model to obtain continuous, noise-robust state estimates. The combined method achieves significantly better localization precision and trajectory stability than TDOA-only baselines. Beyond real-time tracking, our framework offers targeted search-and-recovery capability by predicting post-disconnection motion and explicitly modeling uncertainty growth. The search module differentiates between continued navigation and propulsion failure, allowing search resources to be deployed toward the most probable recovery region. Our framework fuses multi-buoy acoustic data with Kalman filtering and uncertainty propagation to maintain navigation accuracy and yield robust search-region definitions during communication loss.
Zizhan Tang, Yao Liu, Jessica Liu
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA · Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
Localizing an autonomous underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challenge. We present a receiver-passive 3-D localization and spatial mapping system utilizing a single floating surface buoy equipped with a hydrophone array and an inertial measurement unit (IMU). The central difficulty is that surface wave motion induces six-degree-of-freedom (6-DOF) perturbations that rotate the array between snapshots, degrading conventional subspace processing. We resolve this by introducing a fixed-point iterative MUltiple SIgnal Classification algorithm (FP-MUSIC) that uses IMU measurements to de-warp snapshot covariances prior to direction-of-arrival estimation. Furthermore, we employ a subspace-projected wideband matched filter to resolve beacon ranges and use power asymmetry for independent front-back identification. Evaluations across simulated sea states demonstrate that FP-MUSIC substantially reduces localization error relative to uncompensated methods and sustains robust 3-D tracking and vehicle orientation estimation under wave-induced motion. At moderate sea state, FP-MUSIC increases the 2-m beacon-separation accuracy from approximately 45% to 75%.
Usama Saqib, Ola Rønning, Andrzej Wąsowski
Software Quality Research Group, IT University of Copenhagen, Denmark