AUV Control System Research Articles

The development of AUV control system with accommodation to thruster faults

Abstract A new method for the synthesis of AUV control system for high-precision and highly reliable spatial motion is proposed. The feature of these control systems is that they include an additional accommodation circuit that provides timely detection, determination of the magnitude and compensation for the consequences of the appearance of minor faults that occur in the AUV thrusters. The simulation results confirmed the efficiency and high quality of the synthesized systems. Key words Dynamics modeling of system of solids, unmanned underwater vehicle, UUV, AUUV, mobile robot, hyper-redundant robot, computer design of machines and mechanisms, SolidWorks Motion, MCS.ADAMS. Acknowledgements This work was supported by the Russian Foundation for Basic Research (projects 20-38-70161 and 19-08-00347).

Artifi cial Intelligence Technologies in the Autonomous Underwater Vehicle Control Systems

The purpose of the paper is to present the experience of developing algorithms implemented in the AUV control system using artificial intelligence (AI) technologies. The author’s view on the content of the concept of AI in relation to the creation of complex technical systems is presented. Two of the most promising, according to the author, AI technologies are identified: 1) development of the basic algorithm and its improvement based on the results of comprehensive modeling in various operating conditions; 2) creation of a problem-oriented artificial neural network and its deep learning using a large amount of experimentally obtained training material. It is stated that both technologies are quite time-consuming and require a long time to implement. But if the completeness and adequacy of modeling of the conditions in which the system being created is supposed to function plays a key role in modeling technology, then in machine learning technology, the availability of a sufficient amount of training material comes to the fore (in the case of developing vision systems — images of recognizable objects, the number of which can number many thousands).The paper presents the structure of the multi-agent control system of AUV, focuses on the complexity of the tasks it solves and the need to use AI technologies in its creation. It is shown that of all the tasks solved by the AUV control system, AI methods are most in demand for solving two categories of tasks: 1) recognition of the current situation and making an adequate decision in this situation in the interests of completing the route task; 2) search for the designated bottom object among many other bottom objects of natural and artificial origin. The use of AI technologies is demonstrated by the example of the development using a specially created simulation stand of the AUV control algorithm when bypassing an extended. It is proposed to solve the problem of detecting and recognizing a designated bottom object using deep learning technology of a problem-oriented artificial neural network with the peculiarity that the training material is formed programmatically in the form of digital images of the desired bottom object at the output of hydroacoustic, optical and electromagnetic means of monitoring the bottom in various conditions of their observation.

Inspection of the Underwater Industrial Objects Using AUV Based on the Method of Feature Points Video Recognition

Automated inspection of seabed industrial equipment with the help of AUV assumes its navigational referencing to the inspection object with sub-met er accuracy. That accuracy cannot be achieved on the base of standard onboard navigation equipment. It is proposed to use the method of video recognition of feature points (FP) on the inspected equipment to solve this problem. The FPs are preliminarily determined on the models of objects and downloaded into the AUV control system in the form of three-dimensional sets (clouds). Ensuring stable high accuracy during continuous movement of AUV in inspected objects space is realized through regular updating of the coordinate referencing to the objects. The inspection procedure using this method consists of the following steps:- the AUV mission specification (including downloading the FPs descriptions),- launching the AUV and preliminary approach to the inspected objects,- determining the optimal sequence for bypassing the inspected objects,- performing the searching procedure for the next FP and navigational reference to it,- subsequent inspection of the specified fragments of equipment.At the same time, the logic of the mission execution provides for a dynamic change in the inspection plan in case of failure when any of the planned FP are detected. The paper presents the AUV control system algorithms that implement the described inspection scenario. The approach correctness was investigated using the mathematical modeling methods.

Navigation and guidance control system of UNUSAITS AUV based on dynamical system using ensemble kalman filter square root

This study presents the development of navigation system of AUV. It is initially in the form of a nonlinear system model to determine the trajectory for the AUV motion control. The non-linear system model of UNUSAITS AUV is then implemented in the Square Root Ensemble Kalman filter (SR-EnKF) to estimate the trajectory of the AUV. The developed EnKF algorithm covers two types of simulations. The contribution from this paper is trajectory or position estimation of nonlinear UNUSAITS AUV system with 6-DOF AUV model. The result of model estimation is presented based on numeric study and simulation. The first simulation is dedicated to generate 400 ensembles, and the second simulation is to generate 400 ensembles. The second simulation shows that the 500 ensembles give more accurate results. A further examination on the accuracy has been performed by accounting for the RMSE of actual condition and the estimation from the simulation, which yields the range of accuracy between 97 % up to 99%.

AUV control system and sonar target identification

Objectives In order to meet the requirements of underwater search and rescue, exploration, target detection and tracking, Methods we carry out a target identification test for the developed T-SEA Ⅰ AUV, through which a typical frame is selected for the target detection and identification from the acquired moving target images by virtue of the front-view sonar; describe the system composition and working principles of T-SEA Ⅰ AUV; then propose a framework for a control system in which laptop and surface control box act as a surface control unit, PC104 industrial control board acts as the main control unit of an autopilot and the PC104 camera board processes the camera images separately; use three-frame difference method to detected the sonar target, and extract the target characteristics according to a fast ellipse detection algorithm based on Hough transform. Results The lake test shows that the AUV runs reliably and normally and meets the requirements of underwater work; the sonar target identification is good. Conclusions This AUV's control system structure and target identification method can provide a reference for the development of medium and large-scale AUVs and the target identifications in other fields such as video, infrared, etc.

Design of motion control system of pipeline detection AUV

A great number of pipelines in China are in unsatisfactory condition and faced with problems of corrosion and cracking, but there are very few approaches for underwater pipeline detection. Pipeline detection autonomous underwater vehicle (PDAUV) is hereby designed to solve these problems when working with advanced optical, acoustical and electrical sensors for underwater pipeline detection. PDAUV is a test bed that not only examines the logical rationality of the program, effectiveness of the hardware architecture, accuracy of the software interface protocol as well as the reliability and stability of the control system but also verifies the effectiveness of the control system in tank experiments and sea trials. The motion control system of PDAUV, including both the hardware and software architectures, is introduced in this work. The software module and information flow of the motion control system of PDAUV and a novel neural network-based control (NNC) are also covered. Besides, a real-time identification method based on neural network is used to realize system identification. The tank experiments and sea trials are carried out to verify the feasibility and capability of PDAUV control system to complete underwater pipeline detection task.

Kalman-PD Control for Depth System of Autonomous Underwater Vehicle

A Kalman-PD depth controller was designed for depth system of AUV, and the controller was simulated on MATLAB/ SIMULINK. Performance of the controller was observed with actuator saturation, random pitch sensor noise and random fin disturbances. At the same time, response of a conventional PID controller for depth closed loop was also given for comparison. Results show that the Kalman-PD controller gives well performance in dealing with the noisy and disordered depth control system of AUV.

Motion Controller for Autonomous Underwater Vehicle Based on Parallel Neural Network

This paper proposes a novel motion controller for autonomous underwater vehicle based on parallel neural network. The motion controller consists of a real-time part, a self-learning part and a desired state programming part, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. Finally, simulation is carried out on ZS-IV vehicle. The control performance is compared with that of neural network controller with different structures such as normal adaptive neural network. The results show that the AUV motion controller based on parallel neural network has a great possibility to solve the problems in the AUV control system design.

S2 BHCA—Multiple AUVs cooperation oriented control architecture

Oceanographic survey, or other similar applications should be the applications of multiple AUVs. In this paper, the skill & simulation based hybrid control architecture (S2BHCA) as the controller's design reference was proposed. It is a multi-robot cooperation oriented intelligent control architecture based on hybrid ideas. The S2BHCA attempts to incorporate the virtues of the reactive controller and of the deliberative controller by introducing the concept of the “skill”. The additional online task simulation ability for cooperation is supported, too. As an application, a multiple AUV control system was developed with three “skills” for the MCM mission including two different cooperative tasks. The simulation and the sea trials show that simple task expression, fast reaction and better cooperation support can be achieved by realizing the AUV controller based on the S2BHCA.

Adaptive AUV Control for Optimized Swathe Laser Stripe Imaging

As part of a three year collaborative research project funded by EPSRC involving both Cranfield Universe and the University of Plymouth, a Deep Mobile Target (DMT5) torpedo-shaped underwater vehicle has be developed into an AUV called Hammerhead. In addition to several low cost navigation sensors, the AUV has be installed with a laser-assisted imaging and navigation sensor. This sensor is based on the laser stripe illumination (LSI) methodology previously developed at Cranfield University (Tetlow and Allwood, 1995), providing enhance viewing of the seabed below the vehicle. However, it also provides real time altitude and seabed-relative translation velocity data to the navigation system during the mission (Dalgleish et al., 2003), as well as gather images to product a post mission enhanced optical waterfall image of a surveyed area (Dalgleish et al., 2004).For a particular water type and clarity, the optimal stand-off distance (altitude and hence swathe) between the LSI a the seabed will vary. The optimisation problem is essentially the pursuit of a balance between image serve efficiency and image quality. This paper describes the development of an automatic process for monitoring the I image quality to determine the optimum altitude at which to fly, and henceforth instruct the AUV control system do so. In this way, swathe may be optimised for maximum seabed coverage and more efficient use of AUV time. 1 paper also presents simulation results from a genetic algorithm (GA) based model predictive control (MI methodology used to track the required altitude provided by this process.