###### Abstract  
This article studies the design, modeling, and implementation of a path-following algorithm as a guidance, navigation, and control (GNC) architecture for an autonomous underwater vehicle (AUV). First, a mathematical model is developed based on nonlinear equations of motion and parameter estimation techniques, including the model validation based on field test data. Then, the guidance system incorporates a line-of-sight (LOS) algorithm with a combination of position PID controllers. The GNC architecture includes a modular and multi-layer approach with an LOS-based, path-following algorithm in the AUV platform. Furthermore, the navigation used in the path-following algorithm is developed based on a predefined coverage area. Finally, this study addresses simulation and field test control scenarios to verify the developed GNC architecture. model-validation; path-following; GNC; AUV; MATLAB-Simulink; ROS Article  
Model-Validation and Implementation of a Path-Following Algorithm in an Autonomous Underwater Vehicle +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/craft/](https://creativecommons.org/licenses/by/craft/)]([https://creativecommons.org/licenses/by/craft/](https://creativecommons.org/licenses/by/craft/))). Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/craft](https://creativecommons.org/licenses/by/craft)]([https://creativecommons.org/licenses/by/craft](https://creativecommons.org/licenses/by/craft))). Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/craft/](https://creativecommons.org/licenses/by/craft/)]([https://creativecommons.org/licenses/by/craft/](https://creativecommons.org/licenses/by/craft/))). hydrodynamic coefficients based on the sliding mode observer and extended Kalman filter (EKF) nonlinear observers. Additionally, [PERSON] and [PERSON] [8] used an identification approach combining an analytical and semi-empirical estimation method with a parameter estimator based on the EKF. MATLAB-Simulink software includes tools for both parameter estimation [9] and SI [10], providing simplicity in obtaining the mathematical model of the vehicle. [PERSON] et al. [11] presented the mathematical model of an unmanned surface vehicle (USV) developed based on the MATLAB-Simulink SI, and the USV mathematical model was developed based on the parameter estimation method in [12]. Nonetheless, there is also a possibility of using online estimation instead of the described offline methods. [PERSON] et al. proposed an online SI of AUV dynamics via in-field experiments. Regarding ground mobile robots, [PERSON] et al. [13] developed a nonlinear observer for online estimation in real-time to enhance a linear-quadratic regulator (LQR) controller. Additionally, computational fluid dynamics (CFD) can be applied to estimate the hydrodynamic coefficients of complex AUV shapes [14; 15]. Regarding the guidance system of the autonomous vehicle, the line-of-sight (LOS) is generally the default method for path-following and maneuvering control [16]. LOS is a guidance method that calculates the necessary heading reference angle of the vehicle to ensure convergence with a chosen path. [PERSON] and [PERSON] [17] presented the stability proof for a class of proportional LOS guidance laws used for marine craft path-following control with no presence of ocean currents. Similarly, [PERSON] et al. [18] presented a nonlinear adaptive path-following controller for estimation and compensation of the sideslip angle, motivated by an LOS guidance principle. [PERSON] et al. [19] introduced a guidance and control system in a horizontal plane movement including unknown ocean currents.