Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle

Ma, Hui; Mu, Xiaokai

doi:10.3390/s21196406

Cited by 11 publications

(7 citation statements)

References 40 publications

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“…AI technology is required to manipulate the UUV body by adjusting the speed for surge, sway, heave, and the angular speed for pitch and yaw when controlling a UUV. UUV control is a subject that is being intensely addressed [ 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 ]. Adjusting the UUV body underwater is required, where communication is difficult, and the UUV should be recovered well at a limited communication bandwidth.…”

Section: Intelligencementioning

confidence: 99%

“…Recently, methods of setting the shortest path by combining neural networks and maintaining a swarm when using multiple UMVs have attracted attention. In [ 159 ], Hui et al proposed an adaptive navigation algorithm that applies in-depth learning so that the AUV can accurately search, considering the measurement deviation of microelectromechanical system (MEMS) sensors. It uses deep learning to generate low-frequency localization information to correct search errors and uses the χ 2 rule to avoid interference from Doppler velocity log (DVL) outliers when the DVL measurement fails.…”

Section: Intelligencementioning

confidence: 99%

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Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bae

Hong

2023

Sensors

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With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

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Section: Intelligencementioning

confidence: 99%

Section: Intelligencementioning

confidence: 99%

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bae

Hong

2023

Sensors

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“…Spurred by deep learning's state-of-the-art capabilities, researchers have already extended artificial intelligence-based techniques into the field of AUV underwater positioning and path planning. For example, [8] suggests a hybrid pipeline, where a deep learning scheme generates low-frequency position information to correct the error accumulation of the navigation system. Then, the x 2 rule determines if the Doppler velocity log (DVL) measurement fails, and an adaptive filter, exploiting the variational Bayesian method, estimates the navigation information.…”

Section: Introductionmentioning

confidence: 99%

Tracking Control Based on GPS Intelligent Buoy System for an Autonomous Underwater Vehicles Under Measurement Noise and Measurement Delay

Liu

2023

Int J Comput Intell Syst

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This paper deals with the real-time tracking control problem for an autonomous underwater vehicle based on an acoustic-based positioning method, i.e., the so-called GPS intelligent buoy system, which causes inevitable measurement delay. The measurement delay increases the control difficulty and degrades the tracking accuracy. Additionally, the exact modeling for an autonomous underwater vehicle is difficult due to uncertain hydrodynamic parameters. Based on these findings, a model-free control scheme is proposed. In the proposed scheme, the GPS intelligent buoy system provides the position signals without velocity measurements. Considering the measurement noise, a robust exact differentiator is used instead of the traditional numerical differentiation method to obtain the derivatives of position signals, which saves the limited actuator energy of autonomous underwater vehicles. Simulations are performed to verify the validity of the proposed control scheme. The results demonstrate that the proposed control scheme can achieve high timeliness and high tracking accuracy for autonomous underwater vehicles. Compared to the conventional model predictive control, the proposed controller requires 89.7% less average calculation time. In addition, the proposed controller outperforms the conventional proportion-differentiation controller in root-mean-square error by approximately 62.3−80.7%.

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“…Some of the adaptive techniques such as fuzzy logic controller [9][10][11][12], sliding mode controller [13][14][15], model predictive controller [16,17], adaptive controllers [18][19][20][21][22][23][24][25][26], neural network [27][28][29], and intelligent robust control method [30,31] are widely used for the overshoot reduction for an autonomous underwater vehicle. Further, some authors have addressed techniques that are based on machine learning, such as semi-supervised and supervised learning [32], deep learning [33], and reinforcement learning [34,35], which are employed in autonomous underwater vehicles for better depth control, heading control, and tracking of the desired path. Though the model has timevarying and uncertain parameters, the adaptive controller, which is a nonlinear type used in the autonomous underwater vehicle model, achieves better performance.…”

Section: Introductionmentioning

confidence: 99%

Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle

Nayak¹,

Das

Panigrahi

et al. 2023

Mathematics

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In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller’s performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty.

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Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle

Cited by 11 publications

References 40 publications

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Tracking Control Based on GPS Intelligent Buoy System for an Autonomous Underwater Vehicles Under Measurement Noise and Measurement Delay

Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle

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