Fish-like swimming has been attracting scientists and engineers attention since many years resulting in attempts of mathematical description of fish movement and its implementation in many interesting prototypes of underwater vehicles. In this paper, conception of research on simulation, implementation and control of bionic underwater vehicle BUV with undulating propulsion is presented. In the next sections, introduction and mathematical model of bionic underwater vehicle motion are included. Then, the last implementation of the robotic fish called CyberFish, which movement is based on the presented mathematic description is presented and shortly described. In the last sections, conception of research on control system of BUV and conclusions are presented.

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Recently a new type of autonomous underwater vehicle uses artificial fins to imitate the movements of marine animals, e.g. fish. These vehicles are biomimetic and their driving system is an undulating propulsion. There are two main methods of reproducing undulating motion. The first method uses a flexible tail fin, which is connected to a rigid hull by a movable axis. The second method is based on the synchronised operation of several mechanical joints to imitate the tail movement that can be observed among real marine animals such as fish. This paper will examine the first method of reproducing tail fin movement. The goal of the research presented in the paper is to identify the parameters of the one-piece flexible fin kinematics model. The model needs further analysis, e.g. using it with Computational Fluid Dynamics (CFD) in order to select the most suitable prototype for a Biomimetic Underwater Vehicle (BUV). The background of the work is explained in the first section of the paper and the kinematic model for the flexible fin is described in the next section. The following section is entitled Materials and Methods, and includes a description of a laboratory test of a water tunnel, a description of a Vision Algorithm (VA)which was used to determine the positions of the fin, and a Genetic Algorithm (GA) which was used to find the parameters of the kinematic fin. In the next section, the results of the research are presented and discussed. At the end of the paper, the summary including main conclusions and a schedule of the future research is inserted.

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In the recent years, a dynamical development of an underwater robotics has been noticed. One of the newest group of underwater robots are biomimetic underwater vehicles. These vehicles are driven by undulating propulsion imitating fins of underwater creatures, e.g. a fish, a seal, etc. This paper undertakes problem of thrust measurement of new biomimetic underwater vehicle equipped with undulating propulsion. At the beginning, the stand for thrust measurement is described. Then, two constructions of BUVs imitating a fish and a seal are presented. Further, the results of thrust measurement for two different undulating propulsions are inserted. At the end of the paper containing conclusions from performed measurements and foreseen research is included.

Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

The technology of Autonomous Underwater Vehicles (AUVs) is developing in two main directions focusing on improving autonomy and improving construction, especially driving and power supply systems. The new Biomimetic Underwater Vehicles (BUVs) are equipped with the innovative, energy efficient driving system consisting of artificial fins. Because these driving systems are not well developed yet, there are great possibilities to optimize them, e.g. in the field of materials. The article provides an analysis of the propulsion force of the fin as a function of the characteristics of the material from which it is made. The parameters of different materials were used for the fin design and their comparison. The material used in our research was tested in a laboratory to determine the Young’s modulus. For simplicity, the same fin geometry (the length and the height) was used for each type of fin. The Euler–Bernoulli beam theory was applied for estimation of the fluid–structure interaction. This article presents the laboratory test stand and the results of the experiments. The laboratory water tunnel was equipped with specialized sensors for force measurements and fluid–structure interaction analysis. The fin deflection is mathematically described, and the relationship between fin flexibility and the generated driving force is discussed.

In this paper, an investigation on the development of a low-cost passive hydroacoustic system for passive detection of moving vessels to counteract possible collision with an unmanned underwater vehicle is presented. The main goal of this paper is to determine if moving vessels generating hydroacoustic signals/signature are present in the space being searched, and if so, to determine the time delay T between two signals (V 1 (t) and V 2 (t)) and consequently to estimate the bearing on the source of the hydroacoustic signals, for example, screw propellers of a moving vessel. The acoustic signals V 1 (t) and V 2 (t) have been recorded by a two hydrophones mounted in an unmanned underwater vehicle. In practice, signals V 1 (t) and V 2 (t) are heavily corrupted by the additional noise. The noise comes from surrounding environment and from the measurement system errors. Moreover, real signals are often unsteady (nonstationary) and random (stochastic). That is why the different methods have been taken under consideration and the received results have been compared. An analysis has been made for time and frequency domain as well. Due to the planned application of the obstacles detection system in the unmanned underwater vehicle, the algorithm had to be feasible for implementation in digital signal processor.