Autonomous Underwater Vehicle Research Articles

Multitemporal Seagrass Mapping and Monitoring of Posidonia Meadows and Banquettes for Blue Carbon Conservation: The Poseidon Project

Abstract. In the framework of mapping underwater environments, Posidonia Oceanica (PO) is a fundamental indicator for environmental monitoring and related studies and analyses for climate change. The POSEIDON project aims to study, develop and test new methodologies to provide knowledge, as well as ultra-high-resolution (UHR) and beyond ultra-high-resolution (BUHR) mapping and monitoring data for the conservation of the PO and related meadows and banquettes in the Mediterranean area. The project is funded by the National Recovery and Resilience Plan and stems from the recommendation of international organizations (European Environmental Directives, Mediterranean PO Network, International Partnership for Blue Carbon, Intergovernmental Oceanographic Commission of UNESCO in the Save the Wave project). Starting from new integrated survey methods and legacy data, the PO, ichthyofauna and seagrass banquettes will be mapped to understand their evolution and extract indicators and useful trends. Furthermore, the produced information will be stored and shared in a webGIS and a dedicated app to prevent unexpected damages during sailing or other activities. POSEIDON will use imaging sensors (RGB and multi/hyperspectral cameras) to extract information. With specific lighting equipment for significant water depths, these sensors will be conducted (by scuba divers or Autonomous Underwater Vehicles) next to the object to acquire BUHR data. Finally, to monitor the fauna (fish, other species, invertebrates), terrestrial and underwater videogrammetry will be applied using mono and stereo-cameras with object detection algorithms.

Robust optimal tracking control of multiple autonomous underwater vehicles subject to uncertain disturbances

AbstractThis paper considers the problem of robust optimal tracking control of multiple autonomous underwater Vehicles (AUVs) subject to uncertain external disturbances. First, the Takagi‐Sugeno (T‐S) fuzzy based technique is utilized to convert the high‐order nonlinear multi‐AUV system into a series of linearized subsystems. Second, a novel fully distributed sliding mode control (FDSMC) strategy is proposed to attenuate the disturbances. Meanwhile, the leader‐following consensus and the nearly optimization of the energy‐cost function for the multi‐AUV system can be achieved simultaneously through the designed optimal nominal control protocol. Moreover, the proposed control strategy has more mild constraints on the communication topologies. Finally, the effectiveness of the proposed FDSMC strategy is verified by numerical simulation studies.

Underwater Multi-Channel MAC with Cognitive Acoustics for Distributed Underwater Acoustic Networks

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture.

Enhancing Autonomous Underwater Vehicle Decision Making through Intelligent Task Planning and Behavior Tree Optimization

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios.

Multi-vehicle adaptive 3D mapping for targeted ocean sampling.

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

Technological innovations and optimized work methods in subsea maintenance and production

Subsea maintenance and production represent critical aspects of offshore operations, vital for sustaining energy production and ensuring operational efficiency. However, these endeavors face numerous challenges, including the complexities of deepwater environments, harsh weather conditions, and the high costs associated with traditional maintenance methods. To address these challenges, this paper explores the integration of technological innovations and optimized work methods in subsea operations. Technological innovations play a pivotal role in revolutionizing subsea maintenance and production. Remote monitoring and control systems enable real-time data collection and decision-making, enhancing operational visibility and efficiency. Autonomous underwater vehicles (AUVs) offer the capability to conduct inspections and repairs in remote and hazardous environments, reducing human intervention and associated risks. Robotics and automation further streamline maintenance tasks, improving accuracy and reducing downtime. Advanced materials and coatings enhance equipment durability and corrosion resistance, prolonging asset lifecycles and reducing maintenance requirements. In parallel, optimized work methods offer strategic approaches to enhance subsea operations' effectiveness. Predictive maintenance strategies leverage data analytics and machine learning to anticipate equipment failures, enabling proactive interventions and minimizing downtime. Condition-based monitoring facilitates real-time assessment of asset health, enabling timely maintenance interventions and cost savings. Integrated asset management systems provide holistic insights into asset performance and facilitate informed decision-making. Lean operations and continuous improvement methodologies further optimize workflows, driving operational excellence and cost efficiency. Through case studies and industry examples, this paper highlights the successful implementation of technological innovations and optimized work methods in subsea maintenance and production. Furthermore, it explores future trends, regulatory considerations, and the importance of industry collaborations in shaping the future of subsea operations. Ultimately, the integration of these approaches offers a pathway towards enhanced operational efficiency, reduced costs, and sustainable subsea operations. Keywords: Technological Innovations, Optimized Work Methods, Subsea Maintenance and Production.

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Design of tile shaped coil structure for wireless power transmission for underwater vehicle

A pair of tile-shaped transmitting and receiving coils, which is suitable for wireless power transfer system of autonomous underwater vehicle (AUV), is proposed in this paper. Both coils are designed as the tile shape to fit the AUV’s outer shell. And the transmitting coil which is located on the dock is a little larger than the receiving coil that is embedded in the UAV. With the HFSS software, the proposed coil shape was optimized. The performance of the coils was analyzed. By adjusting the coil offset distance and angle. the positioning relationship between the two coils was optimized. A serial-serial compensation topology was established, and an appropriate capacitor was selected to construct the WPT system. The experimental results verified the correctness of the theoretical analysis. The output power reached 30 W and the transmission efficiency reached 68.5%.

Adaptive model-free fault-tolerant control for autonomous underwater vehicles subject to actuator failure

Adaptive model-free fault-tolerant control for autonomous underwater vehicles subject to actuator failure

Multi-AUV Heterogeneous Bipartite Consensus Formation Obstacle Avoidance Algorithm Based on Event Triggering-RMPC Under Measurement-Communication Union Framework

This paper investigates a robust model predictive control-based event triggering (ET-RMPC) for heterogeneous autonomous underwater vehicle (AUV) bipartite consensus formation in obstacle avoidance. Firstly, a scheme of heterogeneous AUV bipartite formation control base on signed graph theory and implicit interactive measure-communication union framework is proposed. Next, a RMPC based on the proposed framework for heterogeneous AUV bipartite consensus formation is designed. Additionally, to alleviate the computational burden, a novel event-triggering mechanism is devised based on the error between the estimated state and actual state of the AUV formation trajectory. Then, the stability of the system and the upper and lower bounds of the trigger interval are analyzed based on innovation and Lyapunov theory. Finally, the effectiveness of the proposed method is validated through bipartite consensus formation tracking and obstacle avoidance scenarios.

Kalman filtering used for passive synthetic aperture

In practice, beamforming-based bearing estimators typically fail to account for the impact of environmental factors on the estimating results, resulting in the so-called mismatch problem. To deal with this issue, this paper reconstructs the bearing estimation as a Kalman filter problem, which emphasizes the role of physical models in underwater acoustic signal processing. Since passive synthetic aperture (PSA) is a spatial process including the temporal evolution of a moving array, it fits a Kalman filtering structure. Kalman filter is applied directly to a lake-test dataset collected by an autonomous underwater vehicle (AUV) shell-mounted array, and the experimental findings demonstrate that the bearing estimation results’ variation is significantly decreased than the conventional beamforming (CBF).

Experimental investigation on wave characteristics due to interference of catamaran demi-hulls in waves

The presence of twin hulls of a catamaran has a strong impact on wave pattern in the whole inner region due to interference between waves generated by each demi-hull. Consequently, wave profiles as well as wave height become complicated and unpredictable in the inner region not only in still water, but also in the presence of waves. Determining wave characteristics between demi-hulls of the catamaran is essential for predicting the operation of an autonomous underwater vehicle (AUV) throughout the launch and recovery (LAR) process. This study performed an experiment to investigate wave characteristics between demi-hulls of the catamaran under regular and irregular waves at various wave directions and speeds. The experiment was designed to measure wave elevation in the inner region between demi-hulls in waves using ultrasonic sensors and capacitance wave probes. The experimental method and setting were verified exacting by comparing the motion response of the catamaran measured by the Qualisys system with numerical simulation using the 3D panel method. Then, wave elevation in the inner region was measured and analyzed to wave height which is a representative parameter of regular waves and to wave spectral density and significant wave height which are representative parameters for irregular waves. These parameters were compared to those of the ocean waves to understand influences of wavelengths, sea states, wave directions, and speeds on wave characteristics between demi-hulls of the catamaran. Plotting of difference from ocean waves indicated the discrepancy in wave characteristics among positions in the inner region, wavelengths, wave directions, sea states, and speeds compared to ocean waves. Results of wave characteristics in the inner region are valuable to analyze motion response and behavior of AUV during LAR process of unmanned surface vehicle (USV).

Numerical simulations of bio-inspired approaches to enhance underwater swimming efficiency

The present study discusses the numerical simulation results of swimming similar to manta rays. The complex three-dimensional kinematics of manta rays were implemented to unravel the intricacies of its propulsion mechanisms by using the discrete vortex method (DVM). The DVM replaces the requirement for a structured grid across the computational domain with a collection of vortex elements. This method simplifies grid generation, especially for intricate geometries, resulting in time and effort savings in meshing complex shapes. By modeling the pectoral fins with discrete panels and utilizing vortex rings to represent circulation and wake, the study accurately computes the pressure distribution, circulation distribution, lift coefficient, and thrust coefficient of the manta ray. This study focuses on the modulation of aerodynamic performance by altering the span length and the length change ratio during the downstroke and upstroke motion (SV). The manta ray's three-dimensional vortex configurations comprise a combination of vortex rings, vortex contrails, and horseshoe vortices. Analysis of the three-dimensional vortex structure indicates the presence of multiple vortex rings and horseshoe vortex rings at higher SV values, while adequate formation of horseshoe vortices is not observed at lower SV values. In terms of propulsive performance, both lift and thrust increase with SV, while the propulsive efficiency demonstrates its peak at SV = 1.75. The analysis reveals that at higher SV values, the net thrust generated primarily originates from the tip of the fins. Moreover, the study illustrates a significant enhancement in propulsive efficiency, particularly in association with optimal Strouhal numbers ranging between 0.3 and 0.4. The key findings of this study may be used in efficient design of agile autonomous underwater vehicles for marine exploration and surveillance applications.

Simulation and Experimental Study of the Suppression of Low-Frequency Flow Noise Signals by a Placoid-Scale Skin

This paper addresses the challenge of mitigating low-frequency flow noise signals in autonomous underwater vehicles through the optimization of a placoid-scale skin. Drawing inspiration from the bio-inspired surface features of cylindrical shell structures, an enhanced design of placoid-scale skin is developed using 3D printing technology. This improved structure effectively reduced boundary layer vortices and wake intensity, thereby contributing to the suppression of low-frequency flow noise signals. Experimental results demonstrate that the notable reduction in low-frequency flow noise within the frequency range of 0–500 Hz, with average noise reduction of approximately 5 dB observed at 150 Hz. This reduction is validated by a combination of numerical simulations and experimental testing, confirming the efficacy of the optimized placoid-scale skin in attenuating the low-frequency flow noise associated with uniformly advancing turbulent boundary layers underwater.

Emerging underwater survey technologies: A review and future outlook

Emerging underwater survey technologies are revolutionizing the way we explore and understand the underwater world. This review examines the latest advancements in underwater survey equipment, highlighting their operational benefits and potential areas for future development. Recent developments in underwater survey technologies have led to significant improvements in accuracy, efficiency, and data quality. Advanced sonar systems, such as multibeam and sidescan sonars, provide high-resolution images of the seafloor, allowing for detailed mapping of underwater features. Autonomous underwater vehicles (AUVs) equipped with sophisticated sensors and cameras enable precise data collection in challenging environments, such as deep-sea areas or complex underwater structures. The operational benefits of these technologies are vast. They allow for faster surveying, reduced costs, and improved safety for personnel. Additionally, the high-quality data obtained from these surveys can lead to better decision-making in various industries, including offshore energy, marine research, and underwater archaeology. Looking ahead, the future of underwater survey technologies is promising. There is a growing interest in developing integrated systems that combine multiple sensors and data processing capabilities to provide a more comprehensive view of underwater environments. Artificial intelligence and machine learning algorithms are also being increasingly utilized to analyze large datasets and extract valuable insights. However, several challenges remain, such as the need for better underwater communication systems, improved battery life for autonomous vehicles, and enhanced data processing capabilities. Addressing these challenges will be crucial for the continued advancement of underwater survey technologies. In conclusion, the latest advancements in underwater survey technologies offer exciting opportunities for enhancing our understanding of the underwater world. By leveraging these technologies and addressing key challenges, we can unlock new possibilities for underwater exploration and research.

Maintenance and transformation method for a multi-AUV dense formation

Accurate formation maintenance and safe formation transformations are significant challenges for multiple autonomous underwater vehicle (multi-AUV) dense formations. To address these problems, an innovative control method for a multi-AUV dense formation is proposed. First, a model predictive controller (MPC) that considers AUV input constraints and external disturbances is designed such that a multi-AUV dense formation can accurately maintain a desired formation while tracking a reference trajectory. After that, at the kinematics level, an optimal path for a safe and efficient multi-AUV dense formation transformation is generated based on the Hungarian method. Furthermore, considering an underactuated and nonlinear AUV dynamics model at the dynamics level, a potential function based on collision avoidance is established. It is added to the MPC objective function to further guarantee the potential of the formation transformation. Finally, a multi-AUV dense formation maintenance simulation shows that the proposed method can guarantee higher trajectory tracking accuracy than other algorithms. A multi-AUV dense formation transformation simulation shows that the proposed method avoids the occurrence of cross paths and a safe distance between AUVs is always maintained. The above results demonstrate that multi-AUV dense formations can achieve accurate maintenance and safe transformations, and the proposed method is feasible and effective.

Research on obstacle avoidance of multi-AUV cluster formation based on virtual structure and artificial potential field method★

The formation of autonomous underwater vehicle (AUV) in the three-dimensional environment is very important for carrying out complex ocean tasks. During formation sailing, it is necessary to consider avoiding obstacles while maintaining the formation. In this paper, an AUV formation obstacle avoidance method based on virtual structure and artificial potential field method is proposed. Firstly, a complete mathematical modeling of AUV is carried out, and a sliding mode position tracker based on virtual position points is designed based on this model. Then the tracking points of each AUV in the formation are calculated according to the expected trajectory and formation feature structure to ensure that the spacing between AUVs meets the requirements of the formation. Finally, the artificial potential field method is introduced into the position tracker to avoid obstacles between the obstacle and the AUV. Five groups of simulation experiments of parallel formation, vertical formation, cross formation, triangular formation and unknown complex environment are designed. The formation scalability of the algorithm is simulated and compared with the virtual leader method in related references. Finally, KKSwarm platform is used to conduct a real simulation in 2D environment to verify the effectiveness of the proposed algorithm.The experimental results show that the proposed method can effectively realize the formation navigation of multiple underwater vehicles and successfully avoid obstacles, which provides an effective method to solve the problem of the formation obstacle avoidance of underactuated AUV.

Inshore and offshore marine migration pathways of Atlantic salmon post-smolts from multiple rivers in Scotland, England, Northern Ireland, and Ireland.

The migratory behavior of Atlantic salmon (Salmo salar) post-smolts in coastal waters is poorly understood. In this collaborative study, 1914 smolts, from 25 rivers, in four countries were tagged with acoustic transmitters during a single seasonal migration. In total, 1105 post-smolts entered the marine study areas and 438 (39.6%) were detected on a network of 414 marine acoustic receivers and an autonomous underwater vehicle. Migration pathways (defined as the shortest distance between two detections) of up to 575 km and over 100 days at sea were described for all 25 populations. Post-smolts from different rivers, as well as individuals from the same river, used different pathways in coastal waters. Although difficult to generalize to all rivers, at least during the year of this study, no tagged post-smolts from rivers draining into the Irish Sea were detected entering the areas of sea between the Hebrides and mainland Scotland, which is associated with a high density of finfish aquaculture. An important outcome of this study is that a high proportion of post-smolts crossed through multiple legislative jurisdictions and boundaries during their migration. This study provides the basis for spatially explicit assessment of the impact risk of coastal pressures on salmon during their first migration to sea.

Using a long‐range autonomous underwater vehicle to find and sample harmful algal blooms in Lake Erie

AbstractCyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these risks requires rapid detection of CyanoHABs and quantification of the toxins they produce. Here, we present a method of using a long‐range autonomous underwater vehicle (LRAUV) equipped with a 3rd‐generation Environmental Sample Processor (3G‐ESP) to search for and adaptively sample areas of high chlorophyll potentially representative of CyanoHAB biomass. In August 2021, this method was used in western Lake Erie. The experiment highlighted the effectiveness of the LRAUV autonomous search‐and‐sample methodology, and demonstrated how an interdisciplinary team located in different states virtually coordinated LRAUV operations and directed sampling activities via Internet connectivity using shared, web‐based situational awareness tools. The advancements made provide a foundation for future work to increase LRAUV autonomy and adaptiveness for CyanoHAB studies and monitoring in both freshwater and marine settings.

Adaptive Whale Optimization Algorithm–DBiLSTM for Autonomous Underwater Vehicle (AUV) Trajectory Prediction

AUVs are autonomous underwater robots equipped with advanced sensors and navigation systems. Due to the complexity and uncertainty of the marine environment, AUVs are susceptible to the effects of the marine environment and may experience communication delays or even accidents. Based on the aforementioned issues, this paper proposes a prediction method for lost AUVs based on an adaptive optimization depth BiLSTM (AWOA-DBiLSTM) neural network model. To enhance prediction accuracy, AWOA-DBiLSTM employs a double BiLSTM to extract AUV features from positional information and physical attitude. Additionally, AWOA-DBiLSTM utilizes a gating mechanism to filter and reset physical attitude feature information to obtain features associated with positional information. After undergoing filtering operations, the physical attitude information of the AUV is fused with the position information to achieve trajectory prediction. For the first time, the differentiation and stratified extraction of AUV data features are presented in this paper. The experimental results demonstrate that the model achieves significant improvements in prediction accuracy and generalization, and the present study is of great significance for application in the task of predicting the trajectories of lost AUVs.