Authors:
Fethi Demim
1
;
Abdenebi Rouigueb
2
;
Hadjira Belaidi
3
;
Ali Messaoui
4
;
Khadir Bensseghieur
4
;
Ahmed Allam
5
;
Mohamed Benatia
2
;
Abdelmadjid Nouri
6
and
Abdelkrim Nemra
1
Affiliations:
1
Laboratory of Guidance and Navigation, Ecole Militaire Polytechnique, Bordj El Bahri, Algiers, Algeria
;
2
Laboratory of Artificial Intelligence and Virtual Reality, Ecole Militaire Polytechnique, Bordj El Bahri, Algiers, Algeria
;
3
Signals and Systems Laboratory, Institute of Electrical and Electronic Engineering, University M’Hamed Bougara of Boumerdes, Algeria
;
4
Laboratory of Complex Systems Control and Simulators, Ecole Militaire Polytechnique, Bordj El Bahri, Algiers, Algeria
;
5
Ecole Nationale Polytechnique, Algiers, Algeria
;
6
Department of Maritime Engineering, Université des Sciences et de la Technologie, USTO, Oran, Algeria
Keyword(s):
Localization, Sonar Data Fusion, Autonomous Underwater Vehicle, Sliding Mode Control.
Abstract:
Navigating underwater environments presents serious challenges in control and localization technology. The successful navigation of uncharted territories requires autonomous maneuvers that achieve goals while avoiding obstacles, posing a significant problem to be addressed. Detection-based control using sensor data and obstacle avoidance technology are vital for the autonomy of Autonomous Underwater Vehicles (AUVs). This study focuses on developing a control method based on Sliding Mode Control (SMC) and utilizing an imaging sonar sensor for obstacle avoidance. The proposed approach includes a controller for pitch and depth control, enabling avoidance of stationary objects. A Gaussian potential function is employed to guide the AUV’s maneuvers and avoid obstructions. Numerous simulation results evaluate the control performance of the AUV in realistic simulation conditions, assessing accuracy and stability. The experimental in simulation results demonstrate the excellent performance o
f our approach in navigating various obstacles such as gentle rise, steep drop-off, and underwater walls, using seafloor environment simulation models.
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