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We present a bionic neural wave network that uses multiple autonomous underwater vehicles to search and acquire intelligent targets in an unknown underwater environment. The neuron pheromone content is arranged according to neural wave diffusion and layer-by-layer energy attenuation, when underwater mesh space based on neural wave diffusion theory was established that the neuron nodes in the neural network structure correspond to obstacles, autonomous underwater vehicles, and targets in the environment. In order to solve the problems of over-allocation and under-allocation of the multi-autonomous underwater vehicles system during the cooperative capture of targets, a redistribution mechanism based on the improved self-organizing map algorithm is implemented and directed to rationalize task distribution. Two different taboo search methods are employed to update the autonomous underwater vehicle path in real time, and the polynomial coefficient solution method is used to fit partial path data. So that the autonomous underwater vehicle trajectory can be obtained and an interceptor position coordinate can be predicted. An auxiliary autonomous underwater vehicle is aimed to replace the intercepted autonomous underwater vehicle and the matching capture points are tracked to ensure the completion of the task so that the full range of hunting targets is identified. In order to simulate an unknown complex underwater environment, obstacles are randomly arranged around the target, the location information of the obstacle, and the target is unknown and unpredictable. Four simulation experiments were performed to verify the accuracy and efficiency of the algorithm under unknown environment. The results show that this algorithm can improve the path update average efficiency by 66% compared with other algorithms. Obviously, this algorithm is reasonable and effective.

Due to the large volume of pipeline transportation, low cost, safety and the reliability, and automatic control, it is widely used in many fields of industrial development and human daily life. Most of the traditional hydraulic pipelines are steel pipes, and their structure is simple. High resistance and high consumption during transportation are not conducive to the sustainable development of society. However, the human vascular system is intricate and has excellent mechanical properties. Built on the review, research on the fluid-solid coupling characteristics of a single bionic pipeline and piping system was carried out. In order to simulate the mechanical characteristics of a fluid conveying pipeline, a fluid-structure coupling model of equation 14 of a single pipeline and the transfer matrix of the pipeline system were established. The mechanical characteristics of the pipeline are studied, and the formula is calculated. The simulation analysis shows that the axial force and flow resistance decrease first and then stabilize with the increase of frequency. Finally, the experimental verification and the results show that the method is both reasonable and effective, because the simulation curve and the experimental curve are consistent in trend.

We introduce a multiple autonomous underwater vehicle (multi-AUV) control strategy to improve the cooperative operation ability in formation control and cluster search operations and to minimize the likelihood of collisions and interference with teammates and obstacles under various complex working conditions. Our approach uses a fusion control strategy with a redistribution mechanism (RM) based on virtual structure and leader–follower formation characteristics to improve the rapidity, stability, and accuracy of the multi-AUVs. The RM improved the rapidity and uniqueness of the random distribution formation by addressing over- and under-allocation. The combination of two (26-point and 8-point) path updating methods based on a hybrid bio-inspired self-organizing map algorithm considerably enhanced the performance of the multi-AUV system. Simulation tests and performance analyses indicated that the 8-point path updating method performed better in terms of rapidity; however, the 26-point path updating method was superior in integrity. The hunting performance of the fusion-controlled multi-AUV formation, was then analyzed to verify the feasibility of real-time formations under dynamic and static mixing of multi-obstacles by considering shape irregularities and the random distribution of multi-obstacles in three-dimensional underwater environments.