Neural networks modeling of autonomous underwater vehicle

Amin, R.; Khayyat, Amir Ali Akbar; Osgouie, Kambiz Ghaemi

doi:10.1109/mesa.2010.5552027

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…The authors tested the proposed methodology using a simulation of an AUV and no real sensory data was considered. Another work that used a simulated AUV to generate training data was presented in [50]. The authors compared an MLP and a recurrent neural network (RNN) architecture, showing a slight improvement of the RNN performance over the MLP.…”

Section: Model Learningmentioning

confidence: 99%

Recent Advances in AI for Navigation and Control of Underwater Robots

et al. 2022

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Purpose of Review The goal of this paper is to review current developments in the area of underwater robotics regarding the use of AI, especially in model learning, robot control, perception and navigation as well as manipulation. Recent Findings AI technologies and advanced control techniques are finding their way into robotics systems to deal with complex and challenging conditions and to equip them with higher levels of autonomy. Summary Although AI techniques and concepts are already a focus area in research on autonomous underwater systems, broad adoption to commercial systems is still in its infancy. Nonetheless, major advances have been done in recent years, especially on integrating different capabilities (perception, navigation, advanced control) in a single system and with first approaches on interaction and autonomous manipulation.

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Section: Model Learningmentioning

confidence: 99%

Recent Advances in AI for Navigation and Control of Underwater Robots

et al. 2022

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“…Some non-linear control technologies, including nonlinear intelligent technologies, such as the backstepping method [12], [13], fuzzy control [14], neural networks [15], [16], sliding mode control [17] and model predictive control [18], have been widely applied to the following control of UUV. However, these non-linear control methods are applied, and additional parameters and iterations are also required during the calculation.…”

Section: Introductionmentioning

confidence: 99%

Dubins-RRT Path Planning and Heading-Vector Control Guidance for a Uuv Recovery

Yan¹,

Hao²,

Zhang³

et al. 2016

International Journal of Robotics and Automation

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When a UUV (unmanned underwater vehicle) completes a specific mission, it needs to return to the recovery platform autonomously safely in an unknown environment. So the works of path planning and guidance control for a UUV are indispensable and significant.In this paper, first a smooth and practical Dubins path back to the recovery platform is planned. In particular, the combined Dubins-RRT path (rapidly exploring random tree based on Dubins path) is introduced with densely spread obstacles. Then the UUV is guided along the Dubins-RRT path to the target by heading-vector control. The algorithm is simple and subjects to the kinematics constraints of UUV. The effectiveness of the algorithm is proved by the simulation in real geographical condition.

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“…Although these controller methods have demonstrated acceptable results, these control methods still face difficulties in tuning the controller gains to maintain overall stability and high-quality response when the control performance degrades due to significant changes in the vehicle dynamics and its environment. The high nonlinearity and time-variance of underwater vehicle dynamics, and unpredictable underwater disturbances such as the fluctuating water currents are the main reasons that make the underwater vehicles such as the underwater glider difficult to control [14][15][16]. Thus, it is highly desirable to design a controller that has a self-tuning and an adaptive ability to deal with these constraints.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Homeostatic-Inspired Motion Controller for a Hybrid-Driven Autonomous Underwater Glider

Isa

Arshad

2015

Jurnal Teknologi

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This paper presents a homeostatic controller algorithm and its performance, which controls motion of a hybrid-driven underwater glider. The homeostatic controller is inspired from a biological process known as homeostasis, which maintains a stable state in the face of massively dynamics conditions. The objective is to obtain a better control performance of the glider motion control system with a presence of disturbance, which is the water current. The algorithm was simulated by using MatlabTM. According to the simulation results, in order to achieve the desired pitch angle, the homeostatic controller was able to optimize the glider’s ballast mass and distance of the glider’s sliding mass by reducing the ballast mass up to 17.7% and shortening the sliding mass distance up to 53.7% when compared with the linear-quadratic regulator (LQR) and model predictive control (MPC). Furthermore, validation analyses of the homeostatic controller performance between the simulation and experimental results have shown very satisfactory performance.

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Neural networks modeling of autonomous underwater vehicle

Cited by 6 publications

References 13 publications

Recent Advances in AI for Navigation and Control of Underwater Robots

Recent Advances in AI for Navigation and Control of Underwater Robots

Dubins-RRT Path Planning and Heading-Vector Control Guidance for a Uuv Recovery

Experimental Analysis of Homeostatic-Inspired Motion Controller for a Hybrid-Driven Autonomous Underwater Glider

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