Evolution of Algorithms and Applications for Unmanned Surface Vehicles in the Context of Small Craft: A Systematic Review
Abstract
:1. Introduction
- A perspective of the evolution of USV studies is presented based on the information obtained with the bibliometrix analysis tool. The bibliometric analysis shows the evolution over time of academic production from 2004. Trends are identified in terms of the number of papers published per year, academic production in the five most relevant journals in which the topic has been published, and the most relevant topics.
- A mapping of the different areas of application in the civil and military fields is carried out.
- A mapping of the different task and types of algorithms used to achieve USV autonomy is carried out.
- A labeled dataset, comprising papers indexed in Scopus and Web of Science, that features six study categories: application sector, autonomy level, application case or area, algorithm type or task, methods developed or implemented, and electronic devices used in the system implementation. This dataset enables a variety of analyses of USV literature.
2. Previous Works of Bibliometric Analysis and Literature Review
2.1. Literature Reviews on the Current State and Trends of USV
2.2. Literature Reviews on Specific Topics Related to USV
3. Methodology
3.1. Databases
3.2. Search Strategy
3.3. Selection Criteria
- Identification: duplicate records.
- Screening: literature reviews, conference reviews, short surveys and weekly magazines.
- Eligibility: items with the following conditions.
- -
- Labeled for the design of USV, hull, propulsion or power supply.
- -
- Works in the field of major vessels.
- -
- Conference or journal papers that are published in other conference or journal with updated or extended versions.
- -
- Items that do not address the focus of the study or do not provide relevant information. This criterion includes works in the preconceptual phase with no clear indication of technological maturity level scaling.
3.4. Data Collection Process
3.5. Selection Process
4. Results
4.1. Classification of Papers According to Study Categories
4.1.1. Application Environments
4.1.2. Autonomy Levels
4.1.3. Application Areas
4.1.4. Algorithm Typologies
4.2. Research Questions Results
4.2.1. What Has Been the Evolution of Studies on USV Regarding the Applications and the Achievement of Autonomous Capabilities?
4.2.2. What Are the General Areas in the Civil and Military Environments Where Unmanned Surface Vehicles Are Used?
4.2.3. What Types of Algorithms Have Been the Subject of Research for Achieving USV Autonomy?
5. Discussion
5.1. Evolution of USV Studies
5.2. Areas of Application in the Military and Civilian Fields
5.3. Types of Algorithms Used for USV Development and Operation
6. Limitations and Future Work
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Review Topics | References | Year |
---|---|---|
Current state and trends of autonomous vessels and USV | [1,3] | 2024 |
[2] | 2023 | |
[5] | 2022 | |
Path planning | [6] | 2024 |
[7] | 2023 | |
[8] | 2021 | |
Path-following control systems | [9] | 2023 |
Adaptive control | [10] | 2024 |
Autonomous docking | [11] | 2024 |
Deep learning in Maritime Autonomous Surface Ships (MASSs) | [12] | 2023 |
Decision making in MASS operations | [13] | 2024 |
Regulation of remotely controlled and autonomous commercial vessels | [14] | 2023 |
Data Source | URL |
---|---|
Scopus | www.scopus.com (accesed on 16 May 2024) |
Web of Science | access.clarivate.com (accesed on 16 May 2024) |
Group | Keywords |
---|---|
Group 1 | boats, boat, small boats, riverboat, small boat, small craft |
Group 2 | Unmanned Surface Vehicles, Unmanned Surface Vehicle, Autonomous Vehicles, Autonomous Surface Vehicles, Unmanned Surface Craft, Unmanned Maritime Vehicle, Remotely Operated Surface Vehicles, Unmanned Surface Vessels, USV |
Database | Algorithm Search |
---|---|
Scopus | ( TITLE-ABS-KEY ( “boats” OR “boat” OR “small boats” OR “riverboat” OR “small boat” OR “small craft” ) AND TITLE-ABS-KEY ( “Unmanned Surface Vehicles” OR “Unmanned Surface Vehicle” OR “Autonomous Surface Vehicles” OR “Unmanned Surface Craft” OR “Unmanned Maritime Vehicle” OR “Remotely Operated Surface Vehicles” OR “Unmanned Surface Vessels” OR “USV” ) ) |
WOS | “boats” OR “boat” OR “small boats” OR “rivercat” OR “small boat” OR “small craft” (All Fields) and “Unmanned Surface Vehicles” OR “Unmanned Surface Vehicle” OR “Autonomous Surface Vehicles” OR “Unmanned Surface Craft” OR “Unmanned Maritime Vehicle” OR “Remotely Operated Surface Vehicles” OR “Unmanned Surface Vessels” OR “USV” (All Fields) |
Data Source | Number of Papers |
---|---|
Scopus | 377 |
Web of Science | 85 |
Total after duplicates removing | 387 |
Category | Number of Labeled Papers |
---|---|
Application Environment | 239 |
Level of Autonomy | 198 |
Application Area | 359 |
Algorithm Type | 338 |
Method Used | 174 |
Electronic Devices | 127 |
Color | Number of Papers | % Over Screened Papers |
---|---|---|
Green | 238 | 66.30 |
Yellow | 28 | 7.80 |
Red | 93 | 25.91 |
Category | Number of Labeled Papers | % Over Included Papers |
---|---|---|
Application Environment | 160 | 67.23 |
Level of Autonomy | 141 | 59.24 |
Application Area | 238 | 100.00 |
Algorithm Type | 225 | 94.54 |
Method Used | 147 | 61.76 |
Electronic Devices | 107 | 44.96 |
Application Environments | Number of Papers |
---|---|
Maritime | 89 |
Lacustrine | 22 |
Coastal | 20 |
Fluvial | 18 |
Other (ponds, pools, reservoirs) | 42 |
Level of Autonomy (IMO) | Number of Papers |
---|---|
Fully autonomous vessel | 126 |
Remotely controlled uncrewed vessel | 23 |
Application Area | Number of Papers |
---|---|
Academic | 128 |
Environmental Monitoring | 30 |
Naval/Security | 24 |
Bathymetry/Cartography | 22 |
Risk and Disaster Management | 13 |
Aquaculture/Fishing | 8 |
Oceanography | 8 |
Hydrography/Hydrology | 5 |
Transportation/Tourism/Ports | 3 |
Algorithm Type | Number of Papers |
---|---|
Data collection | 40 |
Path planning | 32 |
Cooperative robotics systems | 26 |
Obstacle avoidance | 17 |
Environment perception | 16 |
Trajectory tracking | 16 |
Collision avoidance | 15 |
Obstacle detection | 15 |
Path following | 14 |
USV state estimation | 13 |
Control | 11 |
Object detection | 9 |
Position control | 8 |
Target tracking | 8 |
Heading control | 6 |
Motion control | 6 |
Course control | 5 |
Autonomous docking | 4 |
Trajectory planning | 3 |
Target detection | 3 |
Sensor fusion | 2 |
Remote control | 2 |
Heading and speed control | 2 |
Mission planning | 2 |
Target localization | 2 |
Application-specific task | 8 |
Application Area | Number of Papers | References |
---|---|---|
Environmental Monitoring | 30 | [12,32,35,37,41,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73] |
Bathymetry/Cartography | 22 | [38,39,40,41,42,43,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89] |
Risk and Disaster Management | 13 | [20,34,52,88,90,91,92,93,94,95,96,97] |
Aquaculture/Fishing | 8 | [21,98,99,100,101,102,103,104] |
Oceanography | 8 | [36,105,106,107,108,109,110,111] |
Hydrography/Hydrology | 5 | [112,113,114,115,116] |
Transportation/Tourism/Ports | 3 | [117,118,119,120,121] |
Algorithm Type | Papers | References | Subsystem |
---|---|---|---|
Environment perception | 16 | [25,49,82,118,122,123,124,125,126,127,128,129,130,131,132,133] | |
Obstacle detection | 15 | [26,49,122,134,135,136,137,138,139,140,141,142,143,144,145] | |
USV state estimation | 13 | [56,70,119,146,147,148,149,150,151,152,153,154,155] | |
Object detection | 9 | [44,150,156,157,158,159,160,161,162] | Navigation |
Target tracking | 8 | [23,24,160,163,164,165,166,167] | (69) |
Target detection | 3 | [24,168,169] | |
Sensor fusion | 2 | [170,171] | |
Target localization | 2 | [99,172] | |
Path planning | 32 | [20,28,29,36,37,45,47,76,118,128,129,132,141,150,164,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189] | |
Obstacle avoidance | 17 | [25,31,45,121,139,140,156,160,167,181,182,183,186,190,191,192,193] | Guidance |
Collision avoidance | 15 | [27,164,174,179,180,187,190,194,195,196,197,198,199,200,201] | (71) |
Trajectory planning | 3 | [165,193,202] | |
Mission planning | 2 | [203,204] | |
Trajectory tracking | 16 | [25,31,62,98,118,165,193,203,204,205,206,207,208,209,210,211] | |
Path following | 14 | [56,76,77,84,187,189,201,212,213,214,215,216,217,218] | |
Control | 11 | [42,97,104,116,128,150,219,220,221,222,223] | |
Position control | 8 | [23,31,39,112,221,224,225,226] | |
Heading control | 6 | [36,62,227,228,229,230] | Control |
Motion control | 6 | [119,204,212,231,232,233] | (74) |
Course control | 5 | [66,173,217,234,235] | |
Autonomous docking | 4 | [156,236,237,238] | |
Remote control | 2 | [146,239] | |
Heading and speed control | 2 | [43,240] |
Task | Method/Algorithm | Reference |
---|---|---|
Waterline detection/Obstacle detection | Image segmentation | [49] |
Wind speed and direction estimation | Neural networks–Perceptron | [124] |
Estimation of meander parameters | Gaussian filters/Restricted interval Kalman filter | [125] |
Navigable waterway detection | Deep learning-based semantic segmentation/Planar homography/Line detection | [128] |
Background segmentation and change detection | Background subtraction | [130] |
Coastline-water detection and recognition | Line segment detection/coarse-to-fine strategy | [133] |
Obstacle detection | Sensor fusion/Weighted ELM binary classifier | [134] |
Miltimodal perception for obstacle detection | CNN–YOLO V7 | [135] |
LiDAR-based ambient detection | Sensor data fusion/Voxel filtering | [136] |
Hallucinating hidden obstacles | Compositional model | [137] |
Temporal context for obstacle detection | Temporal context extraction from image sequences for ambiguity reduction | [138] |
Obstacle avoidance system | CNN–YOLO V4/Vector Field Histogram (VFH) | [139] |
Obstacle detection/Obstacle distance ranging | Fuzzy Kohonen Network (FKN) | [140] |
Obstacle detection | Segmentation | [141] |
Stereo obstacle detection | Semantic segmentation | [26] |
Real-time stationary obstacle detection and localization | Robust two-step outlier rejection method | [143] |
Real-time obstacle detection | SKIP-ENET segmentation model | [144] |
Small obstacle segmentation/Obstacle map estimation | Efficient semantic segmentation networks/Efficient Multi-Feature Aggregation (MFA) module/Gaussian mixture model-based Feature Separation (FS) loss function/FASNET | [145] |
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Castano-Londono, L.; Marrugo Llorente, S.d.P.; Paipa-Sanabria, E.; Orozco-Lopez, M.B.; Fuentes Montaña, D.I.; Gonzalez Montoya, D. Evolution of Algorithms and Applications for Unmanned Surface Vehicles in the Context of Small Craft: A Systematic Review. Appl. Sci. 2024, 14, 9693. https://doi.org/10.3390/app14219693
Castano-Londono L, Marrugo Llorente SdP, Paipa-Sanabria E, Orozco-Lopez MB, Fuentes Montaña DI, Gonzalez Montoya D. Evolution of Algorithms and Applications for Unmanned Surface Vehicles in the Context of Small Craft: A Systematic Review. Applied Sciences. 2024; 14(21):9693. https://doi.org/10.3390/app14219693
Chicago/Turabian StyleCastano-Londono, Luis, Stefany del Pilar Marrugo Llorente, Edwin Paipa-Sanabria, María Belén Orozco-Lopez, David Ignacio Fuentes Montaña, and Daniel Gonzalez Montoya. 2024. "Evolution of Algorithms and Applications for Unmanned Surface Vehicles in the Context of Small Craft: A Systematic Review" Applied Sciences 14, no. 21: 9693. https://doi.org/10.3390/app14219693
APA StyleCastano-Londono, L., Marrugo Llorente, S. d. P., Paipa-Sanabria, E., Orozco-Lopez, M. B., Fuentes Montaña, D. I., & Gonzalez Montoya, D. (2024). Evolution of Algorithms and Applications for Unmanned Surface Vehicles in the Context of Small Craft: A Systematic Review. Applied Sciences, 14(21), 9693. https://doi.org/10.3390/app14219693