Abstract
This paper focuses the control problem of a nonholonomic autonomous underwater vehicle, moving in the tridimensional space. The dynamic of a body in submarine environments is strongly nonlinear. This implies that classical linear controllers are often inadequate whereby Lyapunov theory is here considered. Methods based in this theory are promising tools to design controllers and are applied to the case of MARES, a small-sized autonomous underwater vehicle. Several controllers based only on Lyapunov theory are determined while others combine linear and nonlinear control theory in order to perform various maneuvers. Aiming to verify the correct performance of controllers, simulations and experiments are carried out.

Abstract
The article presents an estimation approach for navigation of the Modular Autonomous Robot for Environment Sampling (MARES) autonomous underwater vehicle (AUV) based on the range to a single beacon deployed in the operation area. Faculty of Engineering of the University of Porto developed the MARES AUV. Typical MARES missions include environmental sampling and monitoring in which the vehicle sweeps a given area while collecting relevant data. The current localization system uses a long-baseline acoustic system based on two acoustic beacons mounted on surface buoys. Each beacon answers the AUV with an acoustic pulse after having been questioned by the AUV in the same way. The MARES AUV interprets sensor data and generates commands at a constant rate of 10 hertz. While measurements from the depth sensor and the compass are available at each time step, range measurements are performed at a lower frequency due to the speed of propagation of acoustic waves.

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.

Abstract