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Path planning is a prerequisite for autonomous underwater vehicles to perform tasks autonomously. Many shortest distance algorithms are applied, and ocean currents are ignored to plan a short path in distance, which is usually time and energy consuming. In fact, the favourable currents can be exploited while avoiding the opposite ocean flows. Based on the bioinspired neural network architecture, this paper proposes a novel dynamic neural network model to plan the time-saving path in ocean current environments. After that, the path is smoothed by the B-spline algorithm. Analysis of the model shows that it can find out the minimum time path. Many simulations have also been introduced to test the effectiveness of the proposed model, showing good results. The dynamic neural network model has no learning procedure and can run in parallel. It has the advantages of loose parameter restrictions and wide spreading of neural activities. In addition, it has also been proven to be suitable for strong ocean currents. [ABSTRACT FROM AUTHOR]

With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given. [ABSTRACT FROM AUTHOR]

The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs. [ABSTRACT FROM AUTHOR]

There are significant advantages using the autonomous underwater vehicle (AUV) for underwater search. Compared with a single AUV, multi-AUV offers greater efficiency and better stability in underwater search. At the same time, the theoretical and technical level of autonomous navigation and cooperative control of multi-AUV formation is the key to the implementation of the underwater search task. The following key factors are worth discussing in the application of multi-AUV in underwater search: task allocation, path planning, and formation control. The purpose of this paper is to grasp the application and development trend of multi-AUV formation in underwater search, so as to summarize the past, present, and future research and development trends of this investigation field in detail. • To the best of authors' knowledge, this is the first systematic and comprehensive survey of multi-AUV underwater search, and is intended to assist research and development of multi-AUV underwater search applications. • Several aspects of task allocation, path planning, and formation control are considered and reviewed. The strengths, weaknesses, limitations, and challenges of the respective algorithms of the above techniques are explicitly discussed, to gain a deeper understanding of how multi-AUV multi-target search is conducted. • The opportunities and challenges of this research are listed based on the purpose of using multi-AUV underwater multi-target search. [ABSTRACT FROM AUTHOR]

This paper develops a novel adaptive fuzzy sliding mode controller for the diving control of autonomous underwater vehicle (AUV). Unlike most previous AUVs’ control approaches, in this paper, the problem of input saturation constraint of rudder angle is considered, and the new auxiliary systems are proposed. Considering the modeling uncertainty of the dynamic model, an adaptive sliding mode controller based on fuzzy logic system is proposed, and the new update laws of control parameters are given. For the adaptive sliding mode controller design, although the input gain of each subsystem is partially known, only one fuzzy logic system is needed for online identification, which is a distinct difference from the previous adaptive sliding mode controllers. The uniformly ultimately boundedness of tracking error is proven by Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Path planning is an important issue for autonomous underwater vehicle (AUV) underwater mission. In this paper, an optimized fuzzy control algorithm is proposed for three-dimensional (3D) AUV path planning. Based on the sonar model, it can be used for path planning in the complex underwater environment. First, the path planning is established based on the two sonars arranged on the horizontal plane and vertical plane. On the environment information collected by sonars, the virtual acceleration and velocity of AUV in the three-dimensional space can be obtained through the fuzzy system with accelerate/break module which enables AUV to avoid dynamic obstacles automatically. Considering the fuzzy boundary choice has great subjectivity, the generated path cannot guarantee to be optimal. Therefore, in order to deal with this problem, two optimized methods are compared to do the optimization of the fuzzy set. Simulation results indicate that the proposed method can generate an optimal 3D path under three-dimensional underwater environment and can be used in real case in the future. [ABSTRACT FROM AUTHOR]

An integrated multiple autonomous underwater vehicles (multi-AUV) dynamic task assignment and path planning algorithm is proposed for three-dimensional underwater workspace with ocean current. The proposed algorithm in this paper combines biological inspired self-organizing map (BISOM) and a velocity synthesis algorithm (VS). The goal is to control a team of AUVs to visit all targets, while guaranteeing AUV’s motion can offset the impact of ocean currents. First, the SOM neural network is developed to assign a team of AUVs to achieve multiple target locations in underwater environments. Then to avoid obstacle autonomously for each AUV to visit the corresponding target, the biological inspired neurodynamics model (BINM) is used to update weights of the winner of SOM, and realize AUVs path planning autonomously. Lastly, the velocity synthesis algorithm is applied to optimize a path for each AUV to visit the corresponding target in dynamic environment with the ocean current. To demonstrate the effectiveness of the proposed algorithm, simulation results are given in this paper. Undoubtedly, the proposed algorithm is capable of dealing with task assignment and path planning in different environment. The path of the AUV is not affected by the effects of ocean currents and there are no great changes. [ABSTRACT FROM PUBLISHER]

This paper studies the time-varying formation reconfiguration control for multiple underactuated autonomous underwater vehicles (AUVs) with an Euler–Lagrange-like model. The goal is to control a fleet of AUVs to achieve any desired formation shape using a distance-based graph rigidity and affine transformation (GR-AT) algorithm. Firstly, the distance-based graph rigidity with backstepping technology is utilized to solve the formation controlproblem, and we acquire the initial nominal formation. In the sequel, we transform the nominal formation into any desired formation shape using the properties of the affine transformation (including translation, scaling, rotation, shearing, or a combination of them). Even the geometric shapes formed can be constantly changed. The Lyapunov stability theory can ensure the uniform ultimate boundedness of whole distance errors. Finally, several simulation examples with formation reconfiguration are given to validate the efficacy of the designed control methods. Moreover, some experimental results are presented to confirm the validity and applicability of the scheme with four real bionic robot fish. • The underactuated AUVs are considered in this paper. • Graph rigidity and backstepping techniques are employed to achieve formation control. • Affine transformation technique is used to realize formation reconfiguration. • The Lyapunov based stability analysis shows that the proposed controller is stable. • Experimental results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, for the over-actuated Autonomous Underwater Vehicle (AUV) system, a novel tracking controller with thruster fault accommodation is proposed. Firstly, a cascaded control method is proposed for AUV robust tracking control. Then, we deal with the tracking control problem when one or more thrusters are completely or partly malfunctioning. Different control strategies are used to reallocate the thruster forces. For the cases that thrusters are partly malfunctioning, a weighted pseudo-inverse is firstly used to generate the normalised thruster forces. When the normalised thruster forces are out of maximum limits, the Quantum-behaviour Particle Swarm Optimisation (QPSO) is used for the restricted usage of the faulty thruster and to find the solution of the control reallocation problem within the limits. Compared with the weighted pseudo-inverse method, the QPSO algorithm does not need truncation or scaling to ensure the feasibility of the solution due to its particle search in the feasible solution space. The proposed controller is implemented in order to evaluate its performance in different faulty situations and its efficiency is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

A multi-AUV cooperative hunting algorithm based on bio-inspired neural network is proposed for 3-D underwater environment with obstacle. Firstly, AUV 3-D working environment is represented by biological inspired neural network model, and there is one-to-one correspondence between each neuron in neural network and the position of the grid map of underwater environment. Then the activity value of neurons is used to guide each hunting AUV navigation and obstacle avoidance. A high efficient path for each hunting AUV is arranged and finally the target is surrounded by AUVs. Lastly, to demonstrate the effectiveness of the proposed algorithm, simulation results are given in this paper. [ABSTRACT FROM AUTHOR]

Ocean currents impose a negative effect on Autonomous Underwater Vehicle (AUV) underwater target searches, which lengthens the search paths and consumes more energy and team effort. To solve this problem, an integrated algorithm is proposed to realise multi-AUV cooperative search in dynamic underwater environments with ocean currents. The proposed integrated algorithm combines the Biological Inspired Neurodynamics Model (BINM) and Velocity Synthesis (VS) method. Firstly, the BINM guides a team of AUVs to achieve target search in underwater environments; BINM search requires no specimen learning information and is thus easier to apply to practice, but the search path is longer because of the influence of ocean current. Next the VS algorithm offsets the effect of ocean current, and it is applied to optimise the search path for each AUV. Lastly, to demonstrate the effectiveness of the proposed integrated approach, simulation results are given in this paper. It is proved that this integrated algorithm can plan shorter search paths and thus the energy consumption is lower compared with BINM. [ABSTRACT FROM AUTHOR]

A biologically inspired neurodynamics-based tracking controller of underactuated Autonomous Underwater Vehicles (AUV) is proposed in this paper. The proposed control strategy includes a velocity controller with biological neurons and an adaptive sliding mode controller. The biological neurons are embedded into the backstepping velocity controller to eliminate the sharp speed jumps commonly existing in vehicles due to tracking errors changing suddenly. The outputs of the velocity controller are used as the command inputs of the sliding mode controller, and the thruster control constraints problems that are commonly seen in the backstepping control of AUV are solved by the proposed controller. Simulation results show that the control strategy achieved success in smoothly tracking AUV position and velocity. [ABSTRACT FROM PUBLISHER]

A three-dimensional (3-D) neural network model based on bio-inspired neurodynamics is proposed for the path planning of an autonomous underwater vehicle (AUV) in underwater environments. The model is topologically organized according to the 3-D underwater workspace of the AUV. Each neuron in the neural network uniquely represents a discretized subspace in the workspace. The excitatory and inhibitory inputs to the neural network come from the mission of the AUV and obstacles in the workspace respectively. The AUV is globally attracted through excitatory neural activity and the propagation among the network for its mission. Meanwhile, it is locally pushed away by the inhibitory neural activities to avoid collisions. Simulation results show that the proposed 3-D bio-inspired neural network model is suitable for an AUV to plan various paths in a 3-D underwater workspace. [ABSTRACT FROM AUTHOR]

The motion planner determines the autonomy of the autonomous underwater vehicle. However, many studies often ignore the characteristics of autonomous underwater vehicles and the underwater environment, resulting in unreasonable trajectories. To solve this problem, this paper plans the motion force through prediction and optimization to integrate the characteristics of the autonomous underwater vehicle. The fast travel algorithm is modified to take advantage of favorable currents and output arrival times, taking into account the peculiarities of the underwater environment. A novel objective function is proposed, which incorporates the arrival times to imply the requirements of obstacle avoidance and distance optimization. A penalty item is included in the objective function to avoid strong side flow. At the same time, the force and its changing rate limited in appropriate ranges are also included to balance energy consumption. In order to meet the high running time requirements in practical applications, the warm-start improved quantum particle swarm optimization is introduced into force optimization. Simulation studies are performed on a self-developed autonomous underwater vehicle. The method has good application effects in multi-island areas, ocean currents, and dynamic three-dimensional environments. Comparative simulations show that the planner plans shorter paths with good robustness and adaptability. • The planner implies obstacle avoidance and distance minimization with arrival time. • The planner conforms to the requirements of the AUV's models. • An improved QPSO is proposed for the model-predictive optimization. • The planner balances the energy consumption. [ABSTRACT FROM AUTHOR]

In recent years, harbour security protection has received extensive attention. For the moving invasion target interception problem in the underwater environment with ocean current, a new interception algorithm with multiple autonomous underwater vehicles (multi-AUV) called prediction planning interception (PPI) method is proposed in this paper. First, the interception position is determined quickly and simply through the movement tracking of target, and then the intercept path of each AUV in the environment is planned by the artificial potential field method. When the target is intercepted by any AUV, it is considered a successful interception. The AUV can rapidly intercept the moving target whose trajectory is dynamically changing along relatively short path in the changeable and complicated underwater environment by PPI method. The simulation results show that the interception efficiency of the prediction planning interception method is greatly improved compared with the traditional tracking interception method under different conditions. • The first time underwater interception with multi-AUVs for harbour protection. • The interception efficiency is greatly improved compared with traditional approach. • It is both applicable when the AUV velocity is equal or greater than moving target. [ABSTRACT FROM AUTHOR]