1. Introduction

2. Acoustic Sensors

A. Acoustic Sensors for Navigation

As mentioned, these drones are unmanned, which can create a specific set of obstacles. With the removal of a direct pilot of these vehicles, it is of utmost importance for the AUV or ROV to be aware of its surroundings and location at all times. At such depths, what we know as GPS, a global positioning system, is nonfunctional. Therefore, baseline sensors are of utmost importance as the autonomous operation of an AUV requires an onboard navigation system to provide real-time location, high-precision data, and absolute position outputs [5]. There are three categories of underwater baseline positioning systems: long baseline (LBL), short baseline (SBL), and ultrashort baseline (USBL) positioning systems [6]. Each of these methods requires the proximity, the baseline distance, to a transponder that acts as a beacon, hence the acoustic denomination.

Figure 1. Long Baseline Acoustic Sensor System [6].

Figure 1. Long Baseline Acoustic Sensor System [6].

These beacons or transponders communicate with transducers on the UUVs, which aid in determining the exact position. Transducers listen to pings emitted by underwater drones where a distance estimation is obtained by the turnaround time at a specific frequency, and by using algorithms based on recursive least squares, the vehicle can estimate a position [7]. These devices relay pings or an acoustic signal, and since the speed of sound in water is a known value, these sensors measure the time it takes for these signals to be received; hence, those on land or a ship can establish the AUV or ROV’s location.

Figure 2. Acoustic Sensor System and Communications [8].

Figure 2. Acoustic Sensor System and Communications [8].

The LBL systems differ from the USBL and SBL systems as they require communication between multiple transponders, shown in Figure 1. These transponders are placed on the ocean floor at baseline lengths of 100m to 20km. Any underwater vehicle can be localized within the coverage area of the acoustic signal [6]. This method can be expensive and somewhat complicated; thus, the SBL and USBL systems are more favorable. The underwater vehicle would require multiple acoustic receivers, and this arrangement is necessary to detect separate values from the transponder on the ocean floor. Autonomous and remotely operated underwater vehicles, when using short and ultrashort baseline systems, need to determine the distance at which they are from the beacon and the angle or phase at which the replica signal arrives, which determines the direction [7]. The only difference between the USBL and the SBL systems is the relative distance between transducers; they are about 1 meter and 20-50 meters, respectively [6]. The 3D relative position of the underwater drone to the receiver can be established with both distance and direction.

B. Acoustic Sensors for Research

One of the more commonly thought of sensors used in underwater vehicles, manned and unmanned, is sonar. While acoustic sensors can be used for navigation and positioning, they can also be used in research and data collection since these unmanned drones can go to depths incompatible with long-term human exposure. Geological surveys of the ocean floor have been scarcely conducted; it has been said that humans have explored more of what is in space than what is in our oceans due in part to the difficulties of data collection because of the intensely cold temperatures and pressures at the depths necessary for mapping. There are different types of sonar: single-beam, multibeam, and side scan.

Single-beam sonar is one of the earliest technologies to be applied to seafloor exploration. This technology consists of piezoelectric crystals or ceramic transducers to generate and receive acoustic signals; the time difference between such is how the seafloor depth is measured [9]. It works as an echo type of device. The sonar system on board the AUV or ROV will emit sound waves, then travel and hit an object. The system will then reflect those waves to calculate how far from the object. This is how the depths are translated into topographic maps. Single-beam sonars are cheap, easy to use, and trim; thus, they are used widely in marine engineering, resource exploration, and even in finding the Titanic wreck in 1920 [6,9]. However, these sensors can only be used in some circumstances of oceanographic data collection in small, focused areas. Single-beam sonar is much like a flashlight; it can do detailed surveys in focal areas but is not equipped to scan a whole image.

Multibeam sonar transmits a fan of beams at once and receives multiple echoed signals to obtain a greater area of the seafloor. It’s a combination of various single-beam sonars [6]. Low-frequency sound beams are often used in this detection method because they attenuate so slowly that longer distances can be reached. The GeoSwath Plus is a multibeam sonar system integrated into the Remus 100 AUV, and up to 200m depth below the AUV can be measured [9].

Figure 3. Application of Multibeam Sonar for oceanographic mapping [9].

Figure 3. Application of Multibeam Sonar for oceanographic mapping [9].

Side-scan sonar is a happy medium between the two. While single and multibeam sonars require one and over one hundred beams, respectively, side-scan sonars deploy two beams. In submersibles, side-scan sonar transmits signals from both sides using transducers [9]. This combination allows for data to be collected from either side of the underwater crewless vehicle as it travels over specific spans of the ocean floor. This technology produces an image developed by the returned echoes, as raised seafloor leads to a stronger echo, and soft, smooth spreads have a weaker echo [9]. This analysis can help to determine the size and composition of what it scans.

3. Magnetic Sensors

A. Magnetic Sensors for Navigation

One of the ways that these underwater unmanned vehicles can navigate extreme depths is by using a magnetometer. The magnetometer measures the magnetic field variation in three referential axes subtracted from Earth’s magnetic field [7]. These sensors are sensitive; they must measure what is unseen above and under the water. Since they are so responsive, they can be easily interfered with. A magnetometer can be designed as a towed fish and must keep a certain distance from the water vehicle because electromagnetic equipment on larger UUVs will produce electromagnetic interference, too [9]. Even while widely used in the UUV development field, the operation of the sensor is sensitive to the noise caused by other sources like the operation of additional sensors and motors [7]. Within the last five years, there have been advancements in magnetic sensor technology, enabling designers and manufacturers to integrate these magnetometers within the AUV or ROV. A startup company, Ocean Floor Geophysics (OFG), provides a self-compensation magnetometer (SCM) system based on a real-time compensation algorithm, which can be integrated inside AUVs [9].

While these are useful for general position or location knowledge, magnetometers are best used with other sensors. Several factors must be considered in underwater navigation and propulsion, such as a pressure sensor to measure depth, a gyroscope, and an accelerometer to control altitude [7]. It is essential to understand what different sensors are capable of and how they may work in cooperation.

Figure 4. Unmanned Underwater Vehicle magnetometer function [10].

Figure 4. Unmanned Underwater Vehicle magnetometer function [10].

B. Magnetic Sensors for Research and Data Collection

Another application of magnetic sensors is for research and data collection. This circles back to one of the purposes for AUVs and ROVs: the ability to go where manned vehicles cannot go and work below the surface for extended periods. Underwater magnetic sensing has many advantages, including high concealment, robust detection performance, and high positioning accuracy, which is optimal for functioning in complicated environments [6]. These sensors can assist government agencies with security systems capable of tracking signals or moving objects underwater. Research on underwater magnetic sensing technology has been developed to improve high-precision sensors used in electric field generation mechanisms and marine electromagnetic exploration technology [6].

Magnetic sensors can also be employed in object detection tasks. These could be used to detect possible dangerous ferrous objects. They are beneficial for detecting anchors, fishing trawls, and cables [5]. This could benefit ocean conservation and ensure the location of large underwater cables that bring power and communication. For the detection, low-frequency currents are applied to the cables’ conductors, producing a coaxial alternating magnetic field measured by the magnetometers [12].

Figure 5. Autonomous Underwater Vehicle tracking underwater cable with magnetometers [11].

Figure 5. Autonomous Underwater Vehicle tracking underwater cable with magnetometers [11].

4. Bionic Sensors

B. Bionics: Lateral Lines

Lateral lines are a group of sensory organs along the fish body in Figure 7. The lateral line system of fish consists of two main components: the superficial neuromast, which perceives the flow velocity in the near-field environment, and the canal neuromast, which is sensitive to pressure changes in the near-field environment [6]. Almost all species of fish can perceive minute fluid motions utilizing the lateral system; when water flows over the cupula, the deflection is converted to a neural signal. This form of sensing is ideal when implemented within an ROV or AUV, as it is a network that can sense velocity and pressure changes, which can either navigate or collect information.

Figure 6. Components and function of a whisker sensor [13].

Figure 6. Components and function of a whisker sensor [13].

Fish use these organs to assist in navigation in the dark, which is integral for any unmanned vehicle at extreme depths. One approach to mimicking marine life is by utilizing neural networks. The neural network modeling method was built to mimic the fish in a complex environment by training over a long period to complete various activities and behaviors, building neural networks to process the received signals [14]. This is a combination approach of using artificial intelligence to collect, store, and recognize patterns created by the data obtained by sensors on this modeled neural network. There are more specific types of these sensors. The earlier versions would need an external power supply to function, which can unnecessarily add weight and take up unnecessary space on a UUV. One type of sensor is made of piezoelectric materials, which are energy-transforming. When subject to stress, they can generate voltages and do not need an extrinsic power supply, so hydrodynamic flow sensing could be improved [5].

Figure 7. Diagram of the lateral line organs of fish.

Figure 7. Diagram of the lateral line organs of fish.

5. Discussion

References

1. How much of the ocean has been explored?. How much of the ocean has been explored?: Ocean Exploration Facts: NOAA Office of Ocean Exploration and Research. (n.d.). https://oceanexplorer.noaa.gov/facts/explored.html.
2. NASA. (n.d.). Ocean physics at NASA - NASA Science. NASA. https://science.nasa.gov/earth-science/focus-areas/climate-variability-and-change/ocean-physics/.
3. I Mann, J. (2022, July 4). What is driving the demand for underwater drone detection?. Ship Technology. https://www.ship-technology.com/features/what-is-driving-the-demand-for-underwater-drone-detection/?cf-view.
4. Heo, J., Kim, J. and Kwon, Y. (2017) Technology Development of Unmanned Underwater Vehicles (UUVs). Journal of Computer and Communications, 5, 28-35. [CrossRef]
5. Xingxu Zhang, Jian Luo, Xiaobiao Shan, Tao Xie, Binghe Ma, Enhanced performance of bionic ciliary piezoelectric microsensor for hydrodynamic perception, International Journal of Mechanical Sciences, Volume 247,2023, 108187, ISSN0020- 7403. [CrossRef]
6. Yang Cong, Changjun Gu, Tao Zhang, Yajun Gao, Underwater robot sensing technology: A survey, Fundamental Research, Volume 1, Issue 3, 2021, Pages 337-345, ISSN 2667- 3258, (https://www.sciencedir ect.com/science/article/pii/S2667325821000522). [CrossRef]
7. Javier Neira, Cristhel Siros, Richard Huamani, Elfer Machaca, Paola Fonseca, Wilder Nina, “Review on Unmanned Underwater Robotics, Structure Designs, Materials, Sensors, Actuators, and Navigation Control”, Journal of Robotics, vol. 2021, Article ID 5542920, 26 pages, 2021. [CrossRef]
8. Campagnaro, F.; Steinmetz, F.; Renner, B.-C. Survey on Low-Cost Underwater Sensor Networks: From Niche Applications to Everyday Use. J. Mar. Sci. Eng. 2023, 11, 125. [CrossRef]
9. Sun K, Cui W, Chen C. Review of Underwater Sensing Technologies and Applications. Sensors. 2021; 21(23): 7849. [CrossRef]
10. Luo H, Pan M, Du Q, Zhang Q, Hu J, Ding Z. Velocity-Related Magnetic Interference Compensation of Unmanned Underwater Vehicle. Remote Sensing. 2023; 15(17):4164. [CrossRef]
11. Xiang X, Yu C, Niu Z, Zhang Q. Subsea Cable Tracking by Autonomous Underwater Vehicle with Magnetic Sensing Guidance. Sensors. 2016; 16(8):1335. [CrossRef]
12. Y Eleftherakis D, Vicen-Bueno R. Sensors to Increase the Security of Underwater Communication Cables: A Review of Underwater Monitoring Sensors. Sensors. 2020; 20(3):737. [CrossRef]
13. Siyuan Wang, Peng Xu, Xinyu Wang, Jiaxi Zheng, Xiangyu Liu, Jianhua Liu, Tianyu Chen, Hao Wang, Guangming Xie, Jin Tao, Minyi Xu, Underwater bionic whisker sensor based on triboelectric nanogenerator for passive vortex perception, Nano Energy, Volume 97, 2022, 107210, ISSN2211-2855. [CrossRef]
14. Pu Y, Hang Z, Wang G, Hu H. Bionic Artificial Lateral Line Underwater Localization Based on the Neural Network Method. Applied Sciences. 2022; 12(14):7241. [CrossRef]
15. Underwater Sensors: Marine and subsea sensors for ROV, AUV, and UUV. Unmanned Systems Technology. (2023, October 20). https://www.unmannedsystemstechnology.com/expo/underwater-sensors/.
16. Yu, X. Xiang, L. Lapierre, and Q. Zhang, “Robust Magnetic Tracking of Subsea Cable by AUV in the Presence of Sensor Noise and Ocean Currents,” in IEEE Journal of Oceanic Engineering, vol. 43, no. 2, pp.311-322, April 2018. [CrossRef]