scholarly journals Parameters Identification of the Flexible Fin Kinematics Model Using Vision and Genetic Algorithms

2020 ◽  
Vol 27 (2) ◽  
pp. 39-47
Author(s):  
Karolina Jurczyk ◽  
Paweł Piskur ◽  
Piotr Szymak

AbstractRecently 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.

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880173 ◽  
Author(s):  
Ziye Zhou ◽  
Yanqing Jiang ◽  
Ye Li ◽  
Cao Jian ◽  
Yeyi Sun

This article presents a navigation method for an autonomous underwater vehicle being recovered by a human-occupied vehicle. The autonomous underwater vehicle is considered to carry underwater navigation sensors such as ultra-short baseline, Doppler velocity log, and inertial navigation system. Using these sensors’ information, a navigation module combining the ultra-short baseline positioning and inertial positioning is established. In this study, there is assumed to be no communication between the autonomous underwater vehicle and human-occupied vehicle; thus, to obtain the autonomous underwater vehicle position in the inertial coordinate, a conjecture method to obtain the human-occupied vehicle coordinates is proposed. To reduce the error accumulation of autonomous underwater vehicle navigation, a method called one-step dead reckoning positioning is proposed, and the one-step dead reckoning positioning is treated as a correction to combine with ultra-short baseline positioning by a data fusion algorithm. One-step dead reckoning positioning is a positioning method based on the previous time-step coordinates of the autonomous underwater vehicle.


2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092523
Author(s):  
Lei Cai ◽  
Qiankun Sun

The time-varying ocean currents and the delay of underwater acoustic communication have caused the uncertainty of single autonomous underwater vehicle (AUV) tracking target and the inconsistency of multi-AUV coordination, which make it difficult for multiple AUVs to form a hunting alliance. To solve the above problems, this article proposes the multi-AUV consistent collaborative hunting method based on generative adversarial network (GAN). Firstly, the three-dimensional (3D) kinematic model of AUV is established for the underwater 3D environment. Secondly, combined with the Laplacian matrix, the topology of the hunting alliance in the ideal environment is established, and the control rate of AUV is calculated. Finally, using the GAN network model, the control relationship after environmental interference is used as the input of the generative model. The control rate in the ideal environment is used as the comparison object of the discriminative model. Using the iterative training of GAN to generate a control rate that adapts to the current interference environment and combining multi-AUV topological hunting model to achieve successful hunting of noncooperative target, the experimental results show that the algorithm reduces the average hunting time to 62.53 s and the success rate of hunting is increased to 84.69%, which is 1.17% higher than the particle swarm optimization-constant modulus algorithm (PSO-CMA) algorithm.


2018 ◽  
Vol 37 (10) ◽  
pp. 1168-1183
Author(s):  
Bilal Hammoud ◽  
Salah Bazzi ◽  
Elie Shammas ◽  
Mauricio de Oliveira

This paper introduces a new kinematic model to describe the planar motion of an autonomous underwater vehicle moving in constant current flows. The vehicle is modeled as a rigid body moving at maximum attainable forward velocity with symmetric bounds on the control input for the steering rate. The model approximates the effect a flow will induce on the steering rate of the vehicle due to the asymmetric geometry of the vehicle. By imposing restrictions on the magnitude of the flow, the model is then used to characterize and construct the minimum-time paths that guide the vehicle from a given initial to a final configuration in the plane. Algorithms for the time-optimal path synthesis problem are also introduced, along with several simulations to validate the proposed method. Lastly, insights into how one would approach the energy-optimal problem are given, highlighting the fundamental differences in formulation and methods used to solve for the optimal paths.


2009 ◽  
Author(s):  
Giacomo Marani ◽  
Junku Yuh ◽  
Song K. Choi ◽  
Son-Cheol Yu ◽  
Luca Gambella ◽  
...  

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