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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]

In this article, in order to solve the trajectory tracking control problem with the drive saturation (thrust overrun) for the 4500-m human occupied vehicle named “Deep-sea Warrior,” a model predictive adaptive constraint control strategy is put forward. The proposed control strategy mainly consists of two controllers. The first part is a kinematics controller designed by quantum-behaved particle swarm optimization model predictive control method. The second part is a dynamic controller designed by an adaptive algorithm. In order to study the effect of the ocean current disturbance on tracking controller, the ocean current is incorporated into the kinematics and dynamics model of the 4500-m human occupied vehicle. The thrusts of four degrees of freedom under the ocean current are calculated from designed controllers. Then, the thrusts are assigned to six thrusters on the 4500-m human occupied vehicle according to its thruster arrangement. An ocean current observer based on artificial fish proportional-integral control is designed for unknown currents. The simulation results of tracking control in three-dimensional underwater environment are given, which illustrates that the proposed control strategy can not only meet the hardware requirements (drive saturation) but also achieve a stable and efficient tracking control performance because of its constraint to speed and speed increment, the effect of the ocean current on kinematics and dynamics models and the dual feedback mechanism. [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]

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]

In this paper, for the human occupied vehicle (HOV) system, a cascaded dynamic tracking controller for thruster fault is proposed in three-dimensional underwater workspace. Firstly, the control strategies are used to reallocate the thruster forces based on quantum-behaved particle swarm optimization (QPSO) for thruster failure. Secondly, the kinematics controller based on QPSO-model predictive control (MPC) is designed to obtain the speed control signal. QPSO-MPC with speed constrained optimization is proposed to solve speed jump problem caused by thruster failure. Finally, the kinematics controller is used in conjunction with dynamics controller based on the adaptive control to achieve dynamic tracking control which can solve the driving saturation problem caused by thruster failure and tracking disturbance problem by unknown ocean current disturbance. Simulation results showed the proposed dynamic tracking control with fault-tolerant control could realize stable trajectory tracking for thruster failure without the driving saturation in three-dimensional ocean current environment. • The thruster fault-tolerant control method based on QPSO is investigated for HOV. • MPC is employed to realize solution to the speed jump problem. • The speed synthesis method was combined to take the effect of the ocean current into the design of kinematics control law. • For the unknown ocean current, an observer based on adaptive-PI control was designed to estimate it. [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]