A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles

Xu, Chunhui; Wu, Chengdong; Qu, Daokui; Liu, Jian; Wang, Yudong; Shao, Gang

doi:10.1016/j.oceaneng.2019.106146

Cited by 26 publications

(11 citation statements)

References 16 publications

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“…However, it should be noted that all aforementioned researches 5–25 do not consider the combined nuisance factors of hydrodynamic parameter uncertainty along with process and measurement noise. Indeed, for deep-sea AUV platforms, the global positioning system (GPS) is not available, and therefore, AUVs obtain the velocity information for their controller from on-board navigation sensors, 26,27 such as acoustic and optical devices. Nevertheless, these sensors may be heavily affected by noise due to the complex operation conditions and surrounding environment, resulting in low accuracy of the data measured that are input to the AUV controller.…”

Section: Introductionmentioning

confidence: 99%

Position control for an autonomous underwater vehicle under noisy conditions

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Position tracking of an autonomous underwater vehicle is not trivial as its accuracy is affected by process noise, measurement noise, and uncertain hydrodynamic parameters. Thus, in this article, we studied the position control of an autonomous underwater vehicle that operates under largely unbounded system uncertainties and large noise levels and proposed a method that reduces the interference and thereby enhances the overall autonomous underwater vehicle position estimation. Our technique extended the ensemble Kalman filter by combining it with a time-delay estimator to compensate against extended uncertainty of the system dynamics which cannot be solved by the traditional ensemble Kalman filter. Our synthetic simulations demonstrated the effectiveness of the proposed controller, highlighting its appealing position-control accuracy under simultaneous noise and uncertain hydrodynamics parameters. In addition, our simulation results showed that the proposed controller outperforms the conventional time-delay controller by a percentage range of approximately 30.8%–92.6% in terms of root-mean-square error and requires on average less than 88.2% calculation time than the conventional model predictive control.

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Section: Introductionmentioning

confidence: 99%

Position control for an autonomous underwater vehicle under noisy conditions

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…To take advantage of ocean resources, seabed mapping is the ultimate tool that depends on precise sensors and robust navigation fusion algorithms for autonomous underwater vehicles (AUVs) and remotely operated underwater vehicles (ROVs). Navigational accuracy is a key requirement for complex seabed mapping tasks [ 1 ]. However, primary sensors, three-axis gyros, and accelerometers have biases and drifts, which vary with time and are affected by noise.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Shaukat

Ahmed

Iqbal

et al. 2021

Sensors

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The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.

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“…Given that the Global Positioning System (GPS) navigation system does not operate underwater, AUVs estimate their position utilizing acoustic and optical devices that are usually considered to be on-board navigation sensors [21,22]. Both these sensor types feed the positional AUV controller with velocity information from which the traveled distance is derived.…”

Section: Introductionmentioning

confidence: 99%

“…The estimated velocity v(k) can be transformed into the estimated position η(k) by using the kinematics from Equation (21). Finally, the control input τ(k) is obtained by substituting v(k) and η(k) .…”

mentioning

confidence: 99%

Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions

Liu

2021

Applied Sciences

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This paper deals with the discrete-time position control problem for an autonomous underwater vehicle (AUV) under noisy conditions. Due to underwater noise, the velocity measurements returned by the AUV’s on-board sensors afford low accuracy, downgrading its control quality. Additionally, most of the hydrodynamic parameters of the AUV model are uncertain, further degrading the AUV control accuracy. Based on these findings, a discrete-time control law that improves the position control for the AUV trajectory tracking is presented to reduce the impact of these two factors. The proposed control law extends the Ensemble Kalman Filter and solves the problem of the traditional Ensemble Kalman Filter that underperforms when the hydrodynamic parameters of the AUV model are uncertain. The effectiveness of the proposed discrete-time controller is tested on various simulated scenarios and the results demonstrate that the proposed controller has appealing precision for AUV position tracking under noisy conditions and hydrodynamic parameter uncertainty. The proposed controller outperforms the conventional time-delay controller in root-mean-square error by a percentage range of approximately 72.1–97.4% and requires at least 89.5% less average calculation time than the conventional model predictive control.

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A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles

Cited by 26 publications

References 16 publications

Position control for an autonomous underwater vehicle under noisy conditions

Position control for an autonomous underwater vehicle under noisy conditions

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions

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