3D Underwater Environment Research Articles

Deep‐Learning‐Assisted Triboelectric Whisker Sensor Array for Real‐Time Motion Sensing of Unmanned Underwater Vehicle

AbstractAquatic animals can perceive their surrounding flow fields through highly evolved sensory systems. For instance, a seal whisker array understands the hydrodynamic field that allows seals to forage and navigate in dark environments. In this work, a deep learning‐assisted underwater triboelectric whisker sensor array (TWSA) is designed for the 3D motion estimation and near‐field perception of unmanned underwater vehicles. Each sensor comprises a high aspect ratio elliptical whisker shaft, four sensing units at the root of the elliptical whisker shaft, and a flexible corrugated joint simulating the skin on the cheek surface of aquatic animals. The TWSA effectively identifies flow velocity and direction in the 3D underwater environments and exhibits a rapid response time of 19 ms, a high sensitivity of 0.2V/ms−1, and a signal‐to‐noise ratio of 58 dB. The device also locks onto the frequency of the upstream wake vortex, achieving a minimal detection accuracy of 81.2%. Moreover, when integrated with an unmanned underwater vehicle, the TWSA can estimate 3D trajectories assisted by a trained deep learning model, with a root mean square error of ≈0.02. Thus, the TWSA‐based assisted perception holds immense potential for enhancing unmanned underwater vehicle near‐field perception and navigation capabilities across a wide range of applications.

Three-Dimensional Coverage Path Planning for Cooperative Autonomous Underwater Vehicles: A Swarm Migration Genetic Algorithm Approach

Cooperative marine exploration tasks involving multiple autonomous underwater vehicles (AUVs) present a complex 3D coverage path planning challenge that has not been fully addressed. To tackle this, we employ an auto-growth strategy to generate interconnected paths, ensuring simultaneous satisfaction of the obstacle avoidance and space coverage requirements. Our approach introduces a novel genetic algorithm designed to achieve equivalent and energy-efficient path allocation among AUVs. The core idea involves defining competing gene swarms to facilitate path migration, corresponding to path allocation actions among AUVs. The fitness function incorporates models for both energy consumption and optimal path connections, resulting in iterations that lead to optimal path assignment among AUVs. This framework for multi-AUV coverage path planning eliminates the need for pre-division of the working space and has proven effective in 3D underwater environments. Numerous experiments validate the proposed method, showcasing its comprehensive advantages in achieving equitable path allocation, minimizing overall energy consumption, and ensuring high computational efficiency. These benefits contribute to the success of multi-AUV cooperation in deep-sea information collection and environmental surveillance.

Three-Dimensional Path Planning Based on Six-Direction Search Scheme.

In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme.

Virtual-Target based Path-Following in the 3D Underwater Environment

This work focuses on the design of a 3D path-following strategy for under-actuated underwater vehicles. The modeling of the problem relies on the Serret-Frenet apparatus, leading to the definition of the position error dynamics, with respect to a generic smooth curve in the operational 3D space. On the basis of such a model, a virtual-target based non-linear guidance law, derived by the exploitation of Lyapunov methodology, is designed. The theoretical proof and simulation results prove the functionality and reliability of the proposed approach.

State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

Reinforcement learning (RL) is known for its efficiency and practicality in single-agent planning, but it faces numerous challenges when applied to multi-agent scenarios. In this paper, a Super Sampling Info-GAN (SSIG) algorithm based on Generative Adversarial Networks (GANs) is proposed to address the problem of state instability in Multi-Agent Reinforcement Learning (MARL). The SSIG model allows a pair of GAN networks to analyze the previous state of dynamic system and predict the future state of consecutive state pairs. A multi-agent system (MAS) can deduce the complete state of all collaborating agents through SSIG. The proposed model has the potential to be employed in multi-autonomous underwater vehicle (multi-AUV) planning scenarios by combining it with the Soft Actor–Critic (SAC) algorithm. Hence, this paper presents State Super Sampling Soft Actor–Critic (S4AC), which is a new algorithm that combines the advantages of SSIG and SAC and can be applied to Multi-AUV hunting tasks. The simulation results demonstrate that the proposed algorithm has strong learning ability and adaptability and has a considerable success rate in hunting the evading target in multiple testing scenarios.

Three-Dimensional Underwater Path Planning Based on Modified Potential Field Algorithm in Time-Varying Current

Abstract The article addresses the three-dimensional (3D) underwater path planning problem of an autonomous underwater vehicle (AUV) in a time-varying current. A modified artificial potential field algorithm combining the velocity vector synthesis method is proposed to search for the optimal path. The modified potential field (MPF) algorithm is designed to dynamically plan the non-collision path. Meanwhile, this modified method is also proved to be an effective solution to the goals not reachable with obstacles nearby (GNRON), U-shaped trap, and rotation unreachable problems. To offset the influence of time-varying current, the velocity synthesis approach is designed to adjust the AUV movement direction. Besides, considering path planning in the complex underwater environment, the multi-beam forward-looking sonar (FLS) model is used. Finally, simulation studies substantiate that the designed algorithm can implement the AUV path planning effectively and successfully in a 3D underwater environment.

Underwater guidance of distributed autonomous underwater vehicles using one leader

AbstractConsider the case where autonomous underwater vehicles (AUVs) are deployed to monitor a 3D underwater environment. This paper tackles the problem of guiding all AUVs to the destination while not colliding with a priori unknown 3D obstacles. Suppose that among all AUVs, only the leader AUV has an ability of locating itself, while accessing a destination location. A follower, an AUV that is not a leader, has no sensors for locating itself. Every follower can only measure the relative position of its neighbor AUVs utilizing its sonar sensors. Our paper addresses distributed controls, so that multiple followers track the leader while preserving communication connectivity. We design controls, so that all AUVs reach the destination safely, while maintaining connectivity in cluttered 3D environments. To the best of our knowledge, our article is novel in developing 3D underwater guidance controls, so that all AUVs equipped with sonar sensors are guided to reach a destination in a priori unknown cluttered environments. MATLAB simulations are used to validate the proposed guidance scheme in underwater environments with many obstacles.

Using Gaming Technology to Explore and Visualize Management Impacts on Marine Ecosystems

We have developed an approach that connects a complex and widely used scientific ecosystem modeling approach with a game engine for real-time communication and visualization of scientific results. The approach, OceanViz, focuses on communicating scientific data to non-scientific audiences to foster dialogue, offering experimental, immersive approaches to visualizing complex ecosystems whilst avoiding information overload. Within the context of ecosystem-based fisheries management, OceanViz can engage decision makers into the implicit operation of scientific software as an aid during the decision process, and it can be of direct use for public communication through appealing and informative visualizations. Beside a server-client architecture to centralize decision making around an ecosystem model, OceanViz includes an extensive visualization toolkit capable of accurately reflecting marine ecosystem changes through a simulated three-dimensional (3D) underwater environment. Here we outline the ideas and concepts that went into OceanViz, its implementation and its related challenges. We reflect on challenges to scientific visualization and communication as food-for-thought for the marine ecosystem modeling community and beyond.

Hunting Algorithm for Multi-AUV Based on Dynamic Prediction of Target Trajectory in 3D Underwater Environment

In the research of multi-robot systems, multi-AUV (multiple autonomous underwater vehicles) cooperative target hunting is a hot issue. In order to improve the target hunting efficiency of multi-AUV, a multi-AUV hunting algorithm based on dynamic prediction for the trajectory of the moving target is proposed in this article. Firstly, with moving of the target, sample points are updated dynamically to predict the possible position of a target in a short period time by using the fitting of a polynomial, and the safe domain of the moving target, which is a denied area for the hunting AUVs, is built to avoid the target’s escape when it detects AUVs. Secondly, the method of negotiation is adopted to allocate appropriate desired hunting points for each AUV. Finally, the AUVs arrive at desired hunting points rapidly through deep reinforcement learning (DRL) algorithm to achieve hunting the moving target. The simulations show that hunting AUVs can surround the moving target of which the trajectory is unknown rapidly and accurately by the algorithm in the 3D environment with complex obstacles and results obtained is satisfactory.

Weak k-Barrier Coverage Problem in Underwater Wireless Sensor Networks

Most existing works on barrier coverage assume that sensors are deployed in a two-dimensional (2D) long thin belt region, where a barrier is a chain of sensors from one end of the region to the other end with overlapping sensing zones of adjacent sensors. However, the 2D assumption cannot cover all application scenarios, e.g., underwater wireless sensor networks, where sensors are finally distributed over three-dimensional (3D) underwater environment. In this paper, we investigate weak k-barrier coverage problem in underwater wireless sensor networks. We first analyse how to determine whether a deployed underwater wireless sensor network provides 3D weak k-barrier coverage, and propose a novel and effective scheme to transform the 3D weak k-barrier coverage problem into 2D complete k-coverage problem, based on which we devise an O(n2) time algorithm for the 3D weak k-barrier decision problem. Furthermore, we propose a parallel movement manner, based on which an effective algorithm called Hungarian Method-based sensor assignment algorithm (HMB-SAA) is proposed for constructing weak k-barrier coverage while minimizing the total movement distance of all sensors in underwater wireless sensor networks. Simulation results validate the correctness of our analysis, and show that the proposed algorithm outperforms the GreedyMatch algorithm. To the best of our knowledge, this is the first result for 3D weak k-barrier coverage problem in underwater wireless sensor networks.

DENPSO: A Distance Evolution Nonlinear PSO Algorithm for Energy-Efficient Path Planning in 3D UASNs

Energy optimization is a critical issue in three-dimensional (3D) underwater acoustic sensor networks (UASNs). Intelligent path planning can be applied to extend the lifetime of autonomous underwater vehicles (AUVs) which has attracted many researchers' attention as a key component of UASNs in recent years. In this paper, we put forward an algorithm of distance evolution nonlinear particle swarm optimization (DENPSO), aiming at finding an energy-efficient stable path for AUVs in 3D UASNs. First, in order to ensure that the particles fully explore the 3D underwater environment during the evolution process, we convert the inertia weighting factor and learning factor from linearity to nonlinearity. Second, to avoid particles falling into local optimum regions, the particles of the poor search regions are randomly perturbed by the distance evolution factor. Third, we apply the penalty function to describe the energy optimization goal under the obstacles and ocean currents. To quantify the role of obstacle avoidance in the penalty function, each path is divided into several micro-element points based on the cubic spline interpolation method. Then, we propose a degree value factor to measure the micro-element points falling within the obstacle coverage regions. Finally, simulations are finished in 3D underwater environment and the real environment based on regional ocean model system (ROMS). The results show that DENPSO can avoid the obstacles along the eddy current direction, where the energy consumption of algorithm DENPSO is, respectively, reduced by 2.1514e+03 J and 1.049e+07 J compared with the algorithm LPSO in the above-mentioned environment.

A Collision-Free Graph Coloring MAC Protocol for Underwater Sensor Networks

Employing contention-based medium access control (MAC) protocols in underwater sensor networks (UWSNs) is typically costly. This is due to the unique characteristics of underwater acoustic channels, such as long propagation delay, limited bandwidth, and high bit error rate. As a consequence, the contention-based (handshaking and random access-based) MAC protocols do not perform as efficiently as expected. The collision-free approach is therefore considered to achieve high performance by avoiding the collisions at the MAC layer in order to improve energy efficiency, throughput, and fairness. In this paper, we propose, inspired by the graph coloring techniques, a novel energy-conserving and collision-free reservation-based MAC protocol, called GC-MAC, for UWSNs. GC-MAC employs time-division multiple access (TDMA)-like approach by assigning separate time-slots, colors, to every individual sensor node in every two-hop neighborhood. Sensors with the same colors can thus transmit at the same time with no chance of collision. GC-MAC is also able to address the near-far effect, spatial-temporal uncertainty, and hidden/exposed node problems, without requiring code-division multiple access (CDMA) or power adjustment for collision avoidance. The network coverage and connectivity is then discussed to show the effectiveness of using cubes to cover a 3D underwater environment. Our extensive performance study shows that GC-MAC performs well by avoiding collisions to achieve better throughput and energy-efficiency performance compared with those of contention-based protocols. There is also a significant improvement in terms of packet delivery ratio and fairness among the nodes under different operational conditions.

An Improved DSA-Based Approach for Multi-AUV Cooperative Search.

Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In this paper, an improved dolphin swarm algorithm- (DSA-) based approach is proposed, and the search problem is divided into three stages, namely, random cruise, dynamic alliance, and team search. In the proposed approach, the Levy flight method is used to provide a random walk for AUV to detect the target information in the random cruise stage. Then the self-organizing map (SOM) neural network is used to build dynamic alliances in real time. Finally, an improved DSA algorithm is presented to realize the team search. Furthermore, some simulations are conducted, and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the target search task in unknown 3D underwater environment efficiently.

An Adaptive Prediction Target Search Algorithm for Multi-AUVs in an Unknown 3D Environment.

For a target search of autonomous underwater vehicles (AUVs) in a completely unknown three-dimensional (3D) underwater environment, a multi-AUV collaborative target search algorithm based on adaptive prediction is proposed in this paper. The environmental information sensed by the forward-looking sonar is used to judge the current state of view, and the AUV system uses this environmental information to perform the target search task. If there is no target in the field of view, the AUV system will judge whether all sub-regions of the current layer have been searched or not. The next sub-region for searching is determined by the evaluation function and the task assignment strategy. If there are targets in the field of view, the evaluation function and the estimation function of the adaptive predictive optimization algorithm is used to estimate the location of the unknown target. At the same time, the algorithm also can reduce the positioning error caused by the noise of the sonar sensor. In this paper, the simulation results show that the proposed algorithm can not only deal with static targets and random dynamic interference target search tasks, but it can also perform target search tasks under some random AUV failure conditions. In this process, the underwater communication limits are also considered. Finally, simulation experiments indicate the high efficiency and great adaptability of the proposed algorithm.

Efficient AUV Path Planning in Time-Variant Underwater Environment Using Differential Evolution Algorithm

Robust and efficient AUV path planning is a key element for persistence AUV maneuvering in variable underwater environments. To develop such a path planning system, in this study, differential evolution (DE) algorithm is employed. The performance of the DE-based planner in generating time-efficient paths to direct the AUV from its initial conditions to the target of interest is investigated within a complexed 3D underwater environment incorporated with turbulent current vector fields, coastal area, islands, and static/dynamic obstacles. The results of simulations indicate the inherent efficiency of the DE-based path planner as it is capable of extracting feasible areas of a real map to determine the allowed spaces for the vehicle deployment while coping undesired current disturbances, exploiting desirable currents, and avoiding collision boundaries in directing the vehicle to its destination. The results are implementable for a realistic scenario and on-board real AUV as the DE planner satisfies all vehicular and environmental constraints while minimizing the travel time/distance, in a computationally efficient manner.

Hybrid charging scheduling schemes for three-dimensional underwater wireless rechargeable sensor networks

Recent breakthrough in wireless power transfer provides a new paradigm for enabling wireless energy replenishment for wireless rechargeable sensor networks, especially in the underwater environment. In this paper, we first propose the concept of UWRSNs (underwater wireless rechargeable sensor networks) and then develop a series of 3D charging schemes for enhancing charging efficiency, using underwater charging robot mules in three-dimensional charging scenarios. Through constructing the architecture of UWRSNs, we develop a basic charging scheme SCS (Shortest-path Charging Scheme), which minimizes the traveling cost for the charging mules in the 3D underwater environment. Then, ECS (Emergency Charging Scheme) is proposed, which concentrates on serving emergency nodes. After that, a charging algorithm that combines ECS and SCS to collaboratively solve the charging problem, namely, HOCS (Hybrid Optimal Charging Scheme) is developed. At last, experimental simulations are conducted to show the outperformed merits of the proposed scheme. Experimental results demonstrate that our schemes not only save energy and time, but also ensure effective utilization of resources.

Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.

An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting

Cooperative hunting by a multi-AUV system in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To conduct this task, each AUV needs to move quickly without obstacle collisions and cooperate with other AUVs considering the overall interests. In this paper, the heterogeneous AUVs cooperative hunting problem is studied, including two main tasks, namely the search and pursuit of targets, and a novel spinal neural system-based approach is proposed. In the search stage, a partition and column parallel search strategy is used in this paper, and a search formation control algorithm based on an improved spinal neural system is proposed. The presented search algorithm not only accomplishes the search task but also maintains a stable formation without obstacle collisions. In the cooperative pursuit stage, a dynamic alliance method based on bidirectional negotiation strategy and a pursuit direction assignment method based on improved genetic algorithm are presented, which can realize the pursuit task efficiently. Finally, some simulations are conducted and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the hunting tasks in unknown 3D underwater environment efficiently.

Localizability Judgment in UWSNs Based on Skeleton and Rigidity Theory

Underwater sensor networks (UWSNs) have been investigated in a variety of applications such as sea resources reconnaissance, pollution monitoring and tactical monitoring. In 3D underwater environments, it is a key topic to judge the localizability of sensor nodes given known locations of a small set of anchor nodes. In this paper, a novel localizability judgment method for UWSNs is proposed based on rigidity theory. A UWSN is modelled as an undirected graph based on acoustic connectivity. The graph is then reduced to a subgraph with global rigidity, called skeleton, from which the set of localizable sensors can determined. Furthermore, the Analytic Hierarchy Process (AHP) is used to evaluate the localization confidence of localizable sensors. Extensive simulations demonstrate that the proposed localizability judgment method can achieve low false negative rate and high efficiency networks of different sensor numbers and sensor densities. It is also shown to perform well in dynamic networks with relatively low water flow speed.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.