Abstract
This paper advocates for the integration of system identification in graduate-level control system courses using accessible theoretical tools. Emphasising real-world applications, particularly in Remotely Operated Vehicle (ROV), the study proposes ROV as educational platforms for teaching control principles. As a concrete example, the paper presents a graduation course project focusing on designing a depth control system for an ROV, where students derive the model from experimental data. This practical application not only enhances the students skills in system identification but also prepares them for challenges in controlling complex systems in both academic and industrial settings.

Abstract
This paper describes a robotic system to detect and estimate the volume of sediments in underwater wall corners, in scenarios with zero visibility. All detection and positioning is based on data from a scanning sonar. The main idea is to scan the walls and the bottom of the structure to detect the corner, and then use data obtained in the direction of the corner to estimate the presence of sediment accumulation and its volume. Our approach implements an image segmentation to extract range from the surfaces of interest. The resulting data is then employed for relative localization and estimate of the sediment accumulation. The paper provides information about the methodologies developed and data from practical experiments.