Mobile Autonomous Underwater Vehicle Research Articles

Improved BINN-Based Underwater Topography Scanning Coverage Path Planning for AUV in Internet of Underwater Things

Deep understanding the special nature of underwater topography plays an important role for Internet of Underwater Things (IoUT). Nowadays, underwater topography scanning with Autonomous Underwater Vehicle (AUV) has been becoming the chief methodology of knowing Seabed topography and geomorphology. How to design topography scanning trajectory can be mathematically described as a full Coverage Path Planning (CPP) problem. In this paper, facing the complete coverage path planning problem of mobile AUV, a new strategy based on Bio-Inspired Neural Network (BINN) algorithm with improved activity value of each neuron is discussed in detail. The original activity value function in BINN is instead of a piecewise linear function to reduce computational complexity. In addition, to overcome traditional dead-zone problem, an A* path planning based dead-zone escape method along the shorter path as early as possible to the recently uncovered area is described in deep. Extensive simulation results and practical experiments verify the performance of proposed Improved Bio-Inspired Neural Network (IBINN in short) based algorithm.

On Performance Analysis for Random 3D Mobile AUV Networks With Limited Data Buffers

In this paper, we consider a finite network of multiple mobile autonomous underwater vehicles (AUVs) collectingdata in a given region and transferring them to a surface sink. All AUVs patrol a cylindrical region and dynamically control their 3-Dimensional (3D) location according to the random way-point (RWP) mobility model. Our specific interest focuses on the system throughput under different locations of the serving AUV. The following three serving AUV selection policies are investigated: 1)The serving AUV is the closest AUV among all active AUVs; 2) The serving AUV is randomly selected among the active AUVs; 3)The serving AUV is the farthest AUV among all active AUVs. Under the time slotted network scenario, the system throughput is defined as the probability that the sink can successfully recover the data packet from the serving AUV in one time slot. Further, we consider a practical constraint that all AUVs are equipped with limited data buffers. Based on the probability distributions of the distances between the AUVs and the surface sink, this paper presents a mathematical framework to characterize the performance of the random 3D mobile network. With the help of this framework, we obtain the theoretical performance in terms of the system throughput of the surface sink and the average queue delay of any AUV. Finally, extensive simulations are presented to verify the accuracy of our analysis and study the effects of various parameters.

Optimal Adaptive Waveform Design Utilizing an End-to-End Learning-Based Pre-Equalization Neural Network in an UVLC System

Visible light communication (VLC) has emerged as a promising communication method in the special field of 6G, such as intersatellite communication (ISC) and underwater wireless optical communication (UWOC). Green light-emitting diode (LED)-based underwater visible light communication (VLC) is considered a potential candidate for UWOC of mobile autonomous underwater vehicle (AUV) and remotely operated underwater vehicle, particularly in coastal and harbor environments. However, due to bandwidth limitations and variable underwater channels, implementing high-speed underwater VLC remains a challenge. In this paper, a neural network-based auto equalization model (NNAEM) using end-to-end learning is proposed to achieve a high-speed underwater VLC link with a bandwidth-limited green LED. The entire NNAEM includes a neural network-based channel model, a pre-equalization neural network, and a post-equalization neural network. Based on the data-driven channel model, our auto equalization (AE) method can dynamically pre-equalize the discrete multitone (DMT) modulated signal in different bandwidth-limited cases and various nonlinearity cases. An underwater VLC link with a data rate of 3.451 Gbps and a transmission distance of 1.2 m is experimentally demonstrated by employing the proposed NNAEM under the hard decision-forward error correction (HD-FEC) threshold of 3.8 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . The data rate improvement is up to 30.4% compared to other advanced digital signal processing (DSP) methods. The scenario adaptation and robustness of the NNAEM are also verified in the experiments, which demonstrate that the NNAEM can serve an important role in underwater high-speed VLC transmission.

Resident autonomous underwater vehicles for persistent mobile ocean observing

Whether near shore or in the open ocean, stationary ocean observing systems lack the ability to respond spatially to transient and difficult-to-predict phenomena, such as mid-ocean ridge eruptions, instabilities in methane hydrate deposits, turbidite flows, sea–ice interactions, mass wasting events, or changes in coastal currents. Detecting and characterizing these types of events and the processes driving them requires mobile systems available at the time of the event onset. Ideally, mobile autonomous underwater vehicles (AUVs) would permanently be stationed in or near areas where events of interest are likely to occur. Traditionally, due to their dependence on batteries, AUV mission durations are often measured in hours and require costly on-water support vessels and human intervention, making it impractical to sustain a mobile observing presence. However, advances in cabled power/comms and renewable marine energy technologies present significant opportunities for developing persistent mobile subsea systems able to recharge their batteries from fixed underwater infrastructure. In addition to power, these “resident” AUVs will require navigation and data transmission support. Acoustic systems will play key roles as sensing systems, navigation, vehicle homing, data transfer, and advanced robotic operations. This talk will present an overview of the systems and concepts relevant to subsea mobile ocean observing systems along with example applications.

Reimagining Autonomous Underwater Vehicle Charging Stations with Wave Energy

The vast capabilities of autonomous underwater vehicles (AUVs)—such as in assisting scientific research, conducting military tasks, and repairing oil pipelines—are limited by high operating costs and the relative inaccessibility of power in the open ocean. Wave powered AUV charging stations may address these issues. With projected increases in usage of AUVs globally in the next five years, AUV charging stations can enable less expensive and longer AUV missions. This paper summarizes the design process and investigates the feasibility of a wave powered, mobile AUV charging station, including the choice of a wave energy converter and AUV docking station as well as the ability to integrate the charging station with an autonomous surface vehicle. The charging station proposed in this paper meets many different commercial, scientific, and defense needs, including continuous power availability, data transmission capabilities, and mobility. It will be positioned as a hub for AUV operations, enabling missions to run autonomously with no support ship. The potential market for this design is very promising, with an estimated $1.64 million market size just for AUV technologies by 2025.

Stream-Function Based 3D Obstacle Avoidance Mechanism for Mobile AUVs in the Internet of Underwater Things

The mobility manager in the Internet of Underwater Things (IoUT) plays a major role in data acquisition. In this paper, one novel obstacle avoidance method based on fluid mechanics is discussed for autonomous underwater vehicle (AUV) in 3D IoUT. The proposed method utilizes ocean current characteristics simplified as stream-function to design an optimal 3D trajectory with an obstacle avoidance function. 3D stream-function is constructed for spherical and cylindrical obstructions in details. Theoretical analysis of 3D stream-function proves that traditional 2D stream-function can be extended to 3D IoUT space under specific conditions. One path deformation method based on virtual obstacle methodology is proposed to overcome the inherent hysteresis problem of the traditional stream-function based obstacle avoidance design. Moreover, we introduce an energy consumption model for AUV to prove that our obstacle avoidance algorithm can improve energy efficiency if actual ocean currents exist. Extensive simulation results verify that the proposed obstacle avoidance method has the characteristics of curve continuity and smoothness, enhanced obstacle avoidance effects, and high energy efficiency. Therefore, the proposed method meets the actual requirements of multi-obstacle avoidance path planning for AUV in IoUT.

HAS⁴: A Heuristic Adaptive Sink Sensor Set Selection for Underwater AUV-Aid Data Gathering Algorithm.

In this paper, we target solving the data gathering problem in underwater wireless sensor networks. In many underwater applications, it is not quick to retrieve sensed data, which gives us the opportunity to leverage mobile autonomous underwater vehicles (AUV) as data mules to periodically collect it. For each round of data gathering, the AUV visits part of the sensors, and the communication between AUV and sensor nodes is a novel high-speed magnetic-induction communication system. The rest of the sensors acoustically transmit their sensed data to the AUV-visit sensors. This paper deploys the HAS (Heuristic Adaptive Sink Sensor Set Selection) algorithm to select the AUV-visited sensors for the purpose of energy saving, AUV cost reduction and network lifetime prolonging. By comparing HAS with two benchmark selection methods, experiment results demonstrate that our algorithm can achieve a better performance.

Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks

We consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms every other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.

Underwater Wireless Sensor Networks

There are a plenty of unexploited resources that lies underwater that covers almost 75% of the earth.In order to utilise them,the field of underwater wireless sensor networks (UWSN) is attracting the researchers to extend their thoughts in this field. The wireless sensor networks are heavy networks that consist of small low cost sensors that have a large amount of solving ability and energy resources which can be applicable in any type of irregular environments irrespective of changing conditions. Keeping in view of the real-time remote data transferring requirements, underwater acoustic sensor networks (UASN) has been recognised as a preferred network because it satisfies all aspects of data transfer. In UASN, the required availability and recycling of energy resources along with specified utilisation of data with the help of utilized sensor nodes for energy requirements that are necessary are done for the development of further theories in these contexts. Due to these causes, the maximum underwater resources utilisation techniques mainly depends on UAN (Underwater Acoustic Networks).Underwater wireless sensor networks (UWSNs) suitable for applications on submarine detection and monitoring,where nodes collect data with a mobile autonomous underwater vehicle (AUV) via optical communications, and applied accordingly to deal with further approaches. They provide continuous monitoring for various applications like ocean sampling network, pollution monitoring, submarine detection, disaster prevention etc.This paper particularly deals with a brief collection of the UWSN applications and some of the algorithms for the path finding in order to pass maximum valued information(VOI) among the different nodes.

A Localization Based Cooperative Routing Protocol for Underwater Wireless Sensor Networks

Localization is one of the major aspects in underwater wireless sensor networks (UWSNs). Therefore, it is important to know the accurate position of the sensor node in large scale applications like disaster prevention, tactical surveillance, and monitoring. Due to the inefficiency of the global positioning system (GPS) in UWSN, it is very difficult to localize a node in underwater environment compared to terrestrial networks. To minimize the localization error and enhance the localization coverage of the network, two routing protocols are proposed; the first one is mobile autonomous underwater vehicle (MobiL-AUV) and the second one is cooperative MobiL (CO-MobiL). In MobiL-AUV, AUVs are deployed and equipped with GPS and act as reference nodes. These reference nodes are used to localize all the nonlocalized ordinary sensor nodes in order to reduce the localization error and maximize the network coverage. CO-MobiL is presented in order to improve the network throughput by using the maximal ratio combining (MRC) as diversity technique which combines both signals, received from the source and received from the relay at the destination. It uses amplify-and-forward (AF) mechanism to improve the signal between the source and the destination. To support our claims, extensive simulations are performed.

A QoS-Aware Underwater Optimization Framework for Inter-Vehicle Communication using Acoustic Directional Transducers

Underwater acoustic communications consume a significant amount of energy due to the high transmission power (10-50 W) and long data packet transmission times (0.1-1 s). Mobile Autonomous Underwater Vehicles (AUVs) can conserve energy by waiting for the `best' network topology configuration, e.g., a favorable alignment, before starting to communicate. Due to the frequency-selective underwater acoustic ambient noise and high medium power absorption - which increases exponentially with distance - a shorter distance between AUVs translates into a lower transmission loss and a higher available bandwidth. By leveraging the predictability of AUV trajectories, a novel solution is proposed that optimizes communications by delaying packet transmissions in order to wait for a favorable network topology (thus trading end-to-end delay for energy and/or throughput). In addition, the solution proposed - which is implemented and compared with geographic routing solutions and delay-tolerant networking solutions using an emulator that integrates underwater acoustic WHOI Micro-Modems - exploits the frequency-dependent radiation pattern of underwater acoustic transducers to reduce communication energy consumption by adjusting the transducer directivity on the fly.

Coastal Oceanography Using a Small AUV

Bathymetry, current, temperature, and depth (CTD) measurements using a small, mobile, autonomous underwater vehicle (AUV) platform are described. Autonomous surveys of a shallow water column off the east coast of Florida during December 1997 were carried out using a 2.13-m long, 0.53-m maximum diameter Ocean Explorer series AUV, equipped with a 1200-kHz acoustic Doppler current profiler (ADCP) and a CDT package. At a speed of 1‐2 m s21, this AUV can perform preprogrammed missions over a period of several hours, collecting in situ oceanographic data and storing it on an onboard datalogger. The vehicle may also carry sidescan sonar or a custom small-scale turbulence measurement package or other instruments for subsidiary measurements. The versatility of the AUV allows measurement of oceanographic data over a substantial region, the motion of the platform being largely decoupled from that of any surface mother ship. In the missions of 5 and 11 December 1997, ‘‘lawn mower pattern’’ AUV surveys were conducted over 1 km2 regions on the east coast of Florida, north of Fort Lauderdale, at depths of 7 and 3 m, respectively, in a water column where the depth ranged from 10 to 32 m. During 5 December, the region was subjected to a cold front from the northwest. Local wind measurements show presence of up to 10 m s21 winds at temperatures of up to 108‐158C below normal for the time of the year. The fixed ADCP indicates occurrence of significant internal wave activity in the region. The data collected using the mobile AUV are utilized to develop a map of the bottom topography and examine current, temperature, and density variations in the context of the background information from a fixed bottom‐mounted ADCP and Coastal-Marine Automated Network buoys. The work described here is a significant step in the development of an autonomous oceanographic sampling network, illustrating the versatility of an AUV platform. The data collected during the missions described will form part of a bank for information on the impact of a cold front on shallow subtropical waters. The authors expect to repeat the missions during other such fronts.