Learning-based Localization of AUV with Outlier Sensor Data

Lee, Gwonsoo; Lee, Phil-Yeob; Kim, Ho Sung; Lee, Han Sol; Lee, Jihong

doi:10.23919/iccas52745.2021.9649814

Cited by 2 publications

(2 citation statements)

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“…The linear velocities of the vehicle are measured using a Doppler velocity log (DVL), and the angular velocities are measured using an inertial measurement unit (IMU). The position of the vehicle in the world coordinate is estimated using an extended Kalman filter (EKF) that utilizes navigation sensor data from an IMU, a DVL, a depth sensor, a digital compass, and a global navigation satellite system (GNSS) [34][35][36]. The vehicle is equipped with a thruster for forward propulsion, as well as rudder fins and stern fins for lateral and vertical moments, respectively.…”

Section: Controller Design For the Auv 21 Auv Systems And Motion-gove...mentioning

confidence: 99%

“…It is noteworthy that the proposed controller in Equation (15) with Equations ( 17) and ( 18) only requires the first derivative of the state variables and the states themselves. The linear and angular velocities can be measured using IMU and DVL, while the vehicle's position can be obtained through a navigation algorithm such as a Kalman filter, utilizing sensors such as IMU, DVL, the depth sensor, and the digital compass [34]. The advantage of not requiring high-order differentiation of the states is that it helps stabilize the controller in experimental environments.…”

Section: Controller Design Using the Coupled Error Dynamics And Time ...mentioning

confidence: 99%

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An Experimental Study on Trajectory Tracking Control of Torpedo-like AUVs Using Coupled Error Dynamics

Cho

Kang

Kim

et al. 2023

JMSE

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In this paper, we propose a trajectory tracking controller with experimental verification for torpedo-like autonomous underwater vehicles (AUVs) with underactuation characteristics. The proposed controller overcomes the underactuation problem by designing the desired error dynamics in a coupled form using state variables in body-fixed and world coordinates. Unlike the back-stepping control requiring high-order derivatives of state variables, the proposed controller only requires the first derivatives of the states, which can alleviate noise magnification issues due to differentiation. We adopt time delay estimation to estimate the dynamics indirectly using control inputs and vehicle outputs, making the proposed controller relatively easy to apply without requiring the all of the vehicle dynamics. We also address some practical issues that commonly arise in experimental environments: handling measurement noises and actuation limits. To mitigate the effects of noise on the controller, a filtering technique using a moving window average is employed. Additionally, to account for the actuation limits, we design an anti-windup structure that takes into consideration the nonlinearity between the thrusting force and rotating speed of the thruster. We verify the tracking performance of the proposed controller through experimentation using an AUV. The experimental results show that the 3D motion control of the proposed controller exhibits an RMS error of 0.3216 m and demonstrate that the proposed controller achieves accurate tracking performance, making it suitable for survey missions that require tracking errors of less than one meter.

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Section: Controller Design For the Auv 21 Auv Systems And Motion-gove...mentioning

confidence: 99%

Section: Controller Design Using the Coupled Error Dynamics And Time ...mentioning

confidence: 99%

An Experimental Study on Trajectory Tracking Control of Torpedo-like AUVs Using Coupled Error Dynamics

Cho

Kang

Kim

et al. 2023

JMSE

Self Cite

View full text Add to dashboard Cite

show abstract

Moving horizon estimation for localization of mobile robots with measurement outliers

Liu,

He,

Zhao

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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This paper investigates the moving horizon estimation (MHE) problem of mobile robots with measurement outliers. To deal with measurement outliers, the Euclidean distance of measurement error is introduced to detect and remove abnormal data. Then, we use dimension expansion methods to preprocess the data of heterogeneous sensors, such as UWB and IMU. An MHE-based method is proposed that deals with the localization of mobile robots with measurement outliers in the presence of bounded noise. An MHE-based estimator is obtained by solving a regularized least-squares problem. We analyze the convergence of the estimation error system using the properties of norm inequalities, and an upper bound is derived for the estimation error system by using norm inequality. Finally, simulation and experiment examples are given to verify the effectiveness and applicability of the proposed approach.

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Learning-based Localization of AUV with Outlier Sensor Data

Cited by 2 publications

References 5 publications

An Experimental Study on Trajectory Tracking Control of Torpedo-like AUVs Using Coupled Error Dynamics

An Experimental Study on Trajectory Tracking Control of Torpedo-like AUVs Using Coupled Error Dynamics

Moving horizon estimation for localization of mobile robots with measurement outliers

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