Development stages of a semi-autonomous underwater vehicle experiment platform

The design of underwater unmanned vehicles is an interdisciplinary study that includes several fields, such as computational fluid dynamics, modeling and control of systems, robotics, image processing, and electronic card design. The operational cost of such vehicles is high because it is dependent on variable fluid properties like salinity and high pressure while its’ mobility must be resistant to environmental conditions such as undersea. The study describes an operating platform, called Lucky Fin, on which the students can develop various control algorithms and can test and extract hydrodynamic parameters of the underwater vehicle. The platform consists of an underwater vehicle and two testing tanks. The control card, the user control program interface, and a manipulator’s arm are designed to be used for a series of control applications such as depth, heading, target tracking, and capturing. The results of several tests are illustrated in this article.

An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Underwater navigation presents crucial issues because of the rapid attenuation of electronic magnetic waves. The conventional underwater navigation methods are achieved by acoustic equipment, such as the ultra-short-baseline localisation systems and Doppler velocity logs, etc. However, they suffer from low fresh rate, low bandwidth, environmental disturbance and high cost. In the paper, a novel underwater visual navigation is investigated based on the multiple ArUco markers. Unlike other underwater navigation approaches based on the artificial markers, the noise model of the pose estimation of a single marker and an optimal algorithm of the multiple markers are developed to increase the precision of the method. The experimental tests are conducted in the towing tank. The results show that the proposed method is able to localise the underwater vehicle accurately.

Use of a nonlinear controller with dynamic couplings in gains for simulation test of an underwater vehicle model

The article considers a method of examining the influence of dynamic couplings contained in the underwater vehicle model on the movement of this vehicle. The method uses the inertia matrix decomposition and a velocity transformation if the fully actuated vehicle is described in the earth-frame representation. Based on transformed equations of motion, a controller including dynamic couplings in the gain matrices is designed. In the proposed method, the control algorithm is used for the test vehicle dynamics model taking into account disturbances. The approach is useful for simulating the model of an underwater vehicle and improving it, thus avoiding unnecessary experiments or planning them better. The procedure is shown for a full model of an underwater vehicle, and its usefulness is verified by simulation.

Lyapunov-Based Formation Control of Underwater Robots

Today, automatic diving robots are used for research, inspection, and maintenance, extensively. Control of autonomous underwater robots (AUVs) is challenging due to their nonlinear dynamics, uncertain models, and the system underactuation. Data collection using underwater robots is increasing within the oceanographic research community. Also, the ability to navigate and cooperate in a group of robots has many advantages compared with individual navigations. Among them, the effectiveness of using resources, the possibility of robots’ collaboration, increasing reliability, and robustness to defects can be pointed out. In this paper, the formation control of underwater robots has been studied. First, the kinematic model of the AUV is presented. Next, a novel Lyapunov-based tracking control algorithm is investigated for the leader robot. Subsequently, a control law is designed using Lyapunov theory and feedback linearization techniques to navigate a group of follower robots in a desired formation associated with the leader and follow it simultaneously. In the obtained results for different reference paths and various formations, the effectiveness of the proposed algorithm is represented.

A Fault-tolerable Control Scheme for an Open-frame Underwater Vehicle

Open-frame is one of the major types of structures of Remote Operated Vehicles (ROV) because it is easy to place sensors and operations equipment onboard. Firstly, this paper designed a petri-based recurrent neural network (PRFNN) to improve the robustness with response to nonlinear characteristics and strong disturbance of an open-frame underwater vehicle. A threshold has been set in the third layer to reduce the amount of calculations and regulate the training process. The whole network convergence is guaranteed with the selection of learning rate parameters. Secondly, a fault tolerance control (FTC) scheme is established with the optimal allocation of thrust. Infinity-norm optimization has been combined with 2-norm optimization to construct a bi-criteria primal-dual neural network FTC scheme. In the experiments and simulation, PRFNN outperformed fuzzy neural networks in motion control, while bi-criteria optimization outperformed 2-norm optimization in FTC, which demonstrates that the FTC controller can improve computational efficiency, reduce control errors, and implement fault tolerable thrust allocation.