Unmanned Surface Vessels in Marine Surveillance and Management: Advances in Communication, Navigation, Control, and Data-Driven Research
Abstract
1. Introduction
2. Communication Networking
2.1. Communication Methods
2.2. Communication Protocols
2.3. Communication Topologies
3. Navigation
3.1. Global Navigation
3.2. Local Navigation
3.3. Collaborative Navigation
3.4. Autonomous Navigation
4. Control
4.1. Group Control
4.2. Distribution Control
4.3. Adaptive Control
4.4. Collaborative Control
5. Data-Driven Tasks
5.1. Investigation
5.2. Measurement
5.3. Perception
6. Summary and Suggestions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Abbreviation | Full Name | Initial Page |
---|---|---|
USV | Unmanned Surface Vehicle | 1 |
D2QN-JTP | Double Deep Q-Network based Joint Trajectory and Power optimization | 3 |
AUV | Autonomous Underwater Vehicle | 6 |
DQN | Deep Q-Network | 6 |
ME | Magnetoelectric Effect | 6 |
OAM | Orbital Angular Momentum | 6 |
HRI | Human-Robot Interaction | 6 |
DDPG | Deep Deterministic Policy Gradient | 6 |
SNR | Signal-to-Noise Ratio | 6 |
BER | Bit Error Rate | 6 |
DRL | Deep Reinforcement Learning | 6 |
PHY | Physical Layer | 9 |
MAC | Medium Access Control Layer | 9 |
GPS | Global Positioning System | 10 |
INS | Inertial Navigation System | 10 |
DWA | Dynamic Window Approach | 13 |
GNSS | Global Navigation Satellite System | 13 |
AI | Artificial Intelligence | 13 |
SLAM | Simultaneous Localization and Mapping | 13 |
UAV | Unmanned Aerial Vehicle | 13 |
LiDAR-SLAM | Light Detection and Ranging-based Simultaneous Localization and Mapping | 14 |
RMSE | Root Mean Square Error | 14 |
PID | Proportional-Integral-Derivative | 14 |
FBLS | Fast Broad Learning System | 15 |
SVM | Support Vector Machine | 15 |
IMU | Inertial Measurement Unit | 15 |
RMS | Root Mean Square | 15 |
RTS | Rauch–Tung–Striebel smoother | 15 |
GRU | Gated Recurrent Unit | 15 |
RTK | Real-Time Kinematic | 15 |
TFMS | Trajectory Feasibility Management System | 15 |
MPC | Model Predictive Control | 15 |
RL | Reinforcement Learning | 15 |
RRT | Rapidly-Exploring Random Tree | 15 |
MADDPG | Multi-Agent Deep Deterministic Policy Gradient | 15 |
SM | Sliding Mode | 15 |
TOD | Time of Departure | 15 |
BCS | Bayesian Compressive Sensing | 15 |
Faster R-CNN | Faster Region-based Convolutional Neural Network | 15 |
LSTM | Long Short-Term Memory | 15 |
MODD2 | Maritime Object Detection Dataset, version 2 | 15 |
FPS | Frames Per Second | 15 |
MOOS-IvP | Mission Oriented Operating Suite—Interval Programming | 15 |
DNN | Deep Neural Network | 15 |
GAN | Generative Adversarial Network | 15 |
MBZIRC | Mohamed Bin Zayed International Robotics Challenge | 15 |
WaSR | Water-obstacle Separation and Refinement | 15 |
IMU | Inertial Measurement Unit | 15 |
MaSTr1325 | Maritime Semantic Trajectory dataset with 1325 images | 15 |
FRN | Feature Refinement Network | 15 |
MIoU | Mean Intersection over Union | 15 |
MPA | Mean Pixel Accuracy | 15 |
A* | A-star Search Algorithm | 16 |
COLREG | International Regulations for Preventing Collisions at Sea | 18 |
DDQN | Double Deep Q-Network | 20 |
VV-A* | Velocity-Varying A-star | 20 |
AIS | Automatic Identification System | 20 |
ANN | Artificial Neural Network | 20 |
L-F + FT | Leader–Follower with Fault Tolerance | 20 |
NSFQ-RBF | Non-Singular Fast Terminal Sliding Mode Control with Radial Basis Function Neural Network | 20 |
IQPSO | Improved Quantum-behaved Particle Swarm Optimization | 20 |
VO | Velocity Obstacle | 20 |
DZ | Dead Zone | 20 |
NMPC | Nonlinear Model Predictive Control | 21 |
DT | Decision Tree | 21 |
ANFIS | Adaptive Neuro-Fuzzy Inference System | 21 |
DET | Deterministic Control | 21 |
ICM-DDPG | Intrinsic Curiosity Module with Deep Deterministic Policy Gradient | 21 |
LOS | Line-of-Sight Guidance | 21 |
ENDURUNS | Energy-based Durable Unmanned Surveying system | 23 |
COVID-19 | Coronavirus Disease 2019 | 23 |
USV SL-20 | SL20 Autonomous Survey Boat | 23 |
CAC | Cooperative Autonomous Capture | 26 |
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Reference | Methods | Scenario | Indicators | Results | Merit | Limitations |
---|---|---|---|---|---|---|
[9] | Modeling + partial field test | Simulation + lake test | Comm. quality, signal strength, channel gain | Adaptable to USV acoustics | Adaptable to USV acoustics | Only shallow water; complex conditions not covered |
[11] | Adaptive sliding mode + observer | Simulation (multi-AUV formation) | Formation error, convergence speed | Fast convergence, error < 0.2 | Robust under disturbance | Only simulation; lacks sea test; idealized model |
[12] | Power control + game theory | Simulation only | Link utility, eavesdropping rate, power efficiency | Utility +23%, leakage ↓ to 3%, power use +18% | Proactive anti-leak | Ideal channels; no delay/power analysis |
[13] | Dueling DQN trajectory optimization | Simulation (multi-USV comm.) | Throughput, power control | Throughput +28%, better anti-jamming | Power/path optimized | Sim only; no real underwater tests |
[14] | ME device design + perf. test | Air + simulated water | Comm. range, freq. response, signal strength | Up to 30 m, stable freq. response | Mini, low-power | Needs real ocean testing |
[15] | Inverse design (multi-objective) | Sim + water pool test | Pressure distrib., transmission efficiency | Pressure +10 dB, efficiency +20% | Precise OAM beam | Complex design; limited adaptability |
[16] | Lit. review + algorithm classification | No real test (theoretical) | Accuracy, robustness | Max 95% recognition (5-year summary) | Improves HRI | Lacks quantitative/practical analysis |
[17] | Tech review + std. comparison | No direct test | Speed, power, link length | 800+ Gbps, lower energy use | Network planning | Uncertain tech path |
[18] | Field study + energy eval | Arabian Gulf field test | Endurance, data rate | >48 h endurance per mission | Zero-emission patrol | Weather-dependent; limited flexibility |
[19] | DDPG + simulation | Underwater optical sim | SNR, BER, beam offset | SNR +15%, BER ↓ | DRL enhances link | Idealized sim; no noise factors |
Reference | Method | Scenario | Indicators | Results | Merit | Limitations |
---|---|---|---|---|---|---|
[43] | FBLS + Wavelet + SVM | GPS loss sim & test | IMU/GPS error | RMS ↓ 56–82% | Robust low-GPS navigation | Weak in turns, data-dependent |
[44] | RTS + GRU fusion | Blocked & open sea sim | Pos./Speed error | Pos. error ↓ 44.7% | GRU-assisted robust navigation | Complex model, real-time unverified |
[45] | RTK-GNSS/INS | Lake Kłodno survey | Pos./Att. error | <0.941 m pos., <0.1° att. | RTK-validated survey precision | Signal blocking untested |
[46] | TFMS + MPC | Multi-target sim | Safety dist., path dev. | 94% success, <0.5 m error | Global-local hybrid planning | Ideal sim, no comm constraints |
[47] | Dist. RL | No prior sim | Safety, smoothness, energy | 98% avoid rate | Risk-aware robust planning | No sea test, costly training |
[48] | RRT + frontier | Virtual waters | Eff., collision rate | 2× efficiency | Hierarchical efficient search | No dynamics, sea states |
[49] | MADDPG | 2–4 USVs, OpenAI sim | Conv. speed, coll., comm. | ↓ 30% coll., ↑ 20% alloc. | Joint area-path optimization | No delay model, no sea test |
[50] | Decentralized factor graph | 3 USVs dynamic | Localization error | 0.13–0.28 m | Decentralized cluster localization | Unverified for large teams |
[51] | Guided multi-agent RL | Obstacle sim | Success rate, time | 94.6% success, ↓ 21% time | Heuristic-guided target pursuit | Delay/fault tolerance unassessed |
[52] | SM + TOD protocol | Net-limited sim | Error range, collision | ↓ 30% collision | TOD-scheduled bounded estimation | Protocol scope limited |
[53] | Improved SM filter | Bandwidth/attack sim | Error, convergence | Conv. < 0.4 s | BCS-based resilient estimation | No sea test, energy not eval. |
[54] | Faster R-CNN | Coastal test | Detection accuracy | ↑ small target detect. | Onboard deep object detection | View/sample limits |
[55] | DQN + LSTM | Unity sim | Conv. speed, range | ↑ 30% conv., ↑ 25% nav. | LSTM-enhanced partial observability | No field validation |
[56] | ShorelineNet | MODD2 dataset | F-score, FPS | 73.1%, 25 fps | Real-time shoreline detection | Light/weather sensitive |
[57] | Deep tracking | MOOS-IvP sim | Trajectory error | ↓ 19% error | DNN-enhanced tracking accuracy | No field test |
[58] | ResNet + DenseNet | Custom & public data | Detection rate | ↑ detection in waves | Multiscale feature fusion | Small dataset |
[59] | GAN + YOLOv5 | MBZIRC sim | Detection, error | ↑ 28% detect, ↓ 34% error | GAN-based visibility enhancement | No dynamic obstacle test |
[60] | WaSR + IMU | MaSTr1325 + MODD2 | F-measure, FPS | ↑ 14%, 10 fps | IMU-vision fusion segmentation | Limited environment adapt. |
[61] | FRN | 3 ocean datasets | MIoU, MPA | 97.01%, 98.37% | Lightweight multi-feature fusion | Wake confusion errors |
[62] | DWA + Decision Tree | Dynamic sim | Avoid rate, response | ↑ local adaptivity | Classifier-driven path tuning | No sea trial |
Reference | Method | Scenario | Indicators | Results | Merit | Limitations |
---|---|---|---|---|---|---|
[65] | DDQN | Small sim | ↑ Succ, ↓ Len, ↓ Time | ↑ Path adaptivity vs. DQN/A*/RRT | Hybrid swarm optimization | No sea test |
[66] | VV-A* | AIS sim | ↓ Len, ↓ Energy, ↓ Time | ↑ Long-range path quality | AIS-guided global planning | ~AIS quality |
[67] | ANN | Multi-USV sim | ↓ Track err | ↑ Formation, no collision | Sensor-based formation | Delay not tested |
[68] | L-F + FT | Sim | ↓ Recovery time | ↑ Stability | Multi-task DDQN planning | No sea env |
[69] | NSFQ-RBF | Sim | ↓ Conv, ↓ Time | ↑ Smooth, short nav | Leader failure recovery | No sea test |
[71] | MPC | Dyn/static sim | ↓ Track err | ↑ Efficient, less compute | Hierarchical MPC coordination | Simple env |
[72] | IQPSO + VO | Obstacle sim | ↑ Avoid rate | ↑ Local avoid | Quantum-enhanced optimization | No field test |
[73] | Coll.-aware | Obst. sim | ↓ Track err | ↑ Collision-free | Robust dual-layer tracking | Simplified model |
[74] | Topo-Adap. | Net sim | ↑ Accuracy | +10% prec. | Surrounding adaptive coordination | No pkt loss |
[75] | Obsrv. + Perf | Formation sim | ↓ Err, ↑ Conv. | ↑ Rejection | Disturbance-aware control | No real test |
[76] | DRL | Multi-USV sim | ↓ Form err | ↑ 15% acc | Model-free formation | ↑ Train cost, no delay |
[77] | Pinning | Fault sim | ↓ Sync err | ↑ >90% recovery | Fault-tolerant clustering | No fault inject |
[78] | Hetero | Sim | ↓ Time, ↓ Err | ↑ Fixed-time ctrl | Heterogeneous fixed-time control | ~Delay tolerant |
[79] | Presc. + DZ | Sim | Conv < 20 s, ↓ Energy | ↑ Fast, robust | Low-bandwidth robustness | Ideal sim |
[80] | ANN + Perf | Group sim | Err < 0.5 m,<40 s | ↑ Interf. tolerant | Collision-aware containment | Delay/ft? |
[81] | Edge + COLREGs | Dynamic sim | ↑ Succ, ↓ Coll. | ↑ Real-time safety | Edge-assisted avoidance | No sea test |
[82] | NMPC | Multi-sim | ↓ Dev, ↑ Avoid | ↑ Autonomy | DT-guided path reconstruction | Multi-ship weak |
[83] | 3DMap + Fuzzy | Mixed UAV-USV sim | ↑ Track acc | ↑ Boundedness | Hetero fuzzy guidance | No field test |
[84] | DET + L-rule | UAV-USV sim | ↓ Command | ↑ Stability | Low-transmit path ctrl | Idealized comm. |
[85] | DET + SensorTol | Search sim | ↑ Tracking | ↑ Fault-tolerant | Robust fault adaptivity | No real platform |
[86] | ICM-DDPG | Currents sim | ↓ Energy | ↑ Path eff. | Curiosity-guided planning | Only sim verified |
[87] | LOS + Quant | MSV sim/exp | ↑ Track conv. | ↓ Comms | Quantized coop ctrl | Only 2-leader |
[88] | ANFIS | MATLAB sim | ↑ Early warn | ↑ Compliance | Fuzzy COLREGs compliance | No sea test |
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Lv, Z.; Wang, X.; Wang, G.; Xing, X.; Lv, C.; Yu, F. Unmanned Surface Vessels in Marine Surveillance and Management: Advances in Communication, Navigation, Control, and Data-Driven Research. J. Mar. Sci. Eng. 2025, 13, 969. https://doi.org/10.3390/jmse13050969
Lv Z, Wang X, Wang G, Xing X, Lv C, Yu F. Unmanned Surface Vessels in Marine Surveillance and Management: Advances in Communication, Navigation, Control, and Data-Driven Research. Journal of Marine Science and Engineering. 2025; 13(5):969. https://doi.org/10.3390/jmse13050969
Chicago/Turabian StyleLv, Zhichao, Xiangyu Wang, Gang Wang, Xuefei Xing, Chenlong Lv, and Fei Yu. 2025. "Unmanned Surface Vessels in Marine Surveillance and Management: Advances in Communication, Navigation, Control, and Data-Driven Research" Journal of Marine Science and Engineering 13, no. 5: 969. https://doi.org/10.3390/jmse13050969
APA StyleLv, Z., Wang, X., Wang, G., Xing, X., Lv, C., & Yu, F. (2025). Unmanned Surface Vessels in Marine Surveillance and Management: Advances in Communication, Navigation, Control, and Data-Driven Research. Journal of Marine Science and Engineering, 13(5), 969. https://doi.org/10.3390/jmse13050969