Contribution to the development of a control architecture for the autonomous navigation of a surface drone

Abstract

This thesis presents the design, implementation, and validation of an autonomous control architecture for an Unmanned Surface Vehicle (USV) developed for dam inspection and environmental monitoring appli- cations. The proposed system, named AquaRob, integrates a cost-effective hardware platform with a modular ROS2-based software framework to enable robust navigation in GPS-degraded and hydrodynam- ically disturbed environments. The hardware architecture combines low-cost sensors, such as the GY-87 IMU and the NEO-M8N GNSS, with a dual-processor setup consisting of an Arduino Due and a Raspberry Pi 5. Sensor data are fused using a dual Extended Kalman Filter (EKF) for precise state estimation. Two complementary control strategies are implemented and evaluated: a Sliding Mode Controller (SMC) for robust path tracking under model uncertainties, and an adaptive PID controller that employs online system identification based on the Recursive Least Squares (RLS) algorithm. These control approaches are validated in a Gazebo-based simulation environment, demonstrating reliable trajectory tracking, stability,

and robustness to disturbances. The results confirm that the integration of model-based and adaptive data- driven control enhances the system’s autonomy and adaptability in complex aquatic environments. The developed framework provides a scalable foundation for future field deployments and extensions toward 3D navigation and multi-agent cooperation in autonomous maritime systems.