Abstract
This papers addresses the dynamic characterization of the autonomous underwater vehicle MARES. The paper presents the main dynamic properties of this underwater robotic platform as well as the procedures employed to obtain the parameters that define the vehicle model. Furthermore, the paper also presents a detailed characterization of the elementary motions that this vehicle is able to perform.

Abstract
This paper describes the interaction between the kinematic model of the AUV MARES and the measurement or observation of the environment through images obtained with a sonar. Three types of sonar are discussed in this paper: forward-look, side scan and multibeam - but the sonar used to develop this work was the side scan sonar. The type of observations and characteristics of the environment provided by the sonar are described here. The method which connects the sensory part of the vehicle with the observations from the sonar, was the Kalman filter (EKF). In this paper, we present two simulations of filters for two different characteristics. Both filters estimate the characteristics of the natural landmarks, creating an environment map, but both of them consider different states of the vehicle. Results of the simulation are obtained. The features that are considered are an underwater pipe on the floor and a vertical wall. A control loop for the vehicle that provides the capacity to move along the feature/landmark from a reference distance is also discussed.

Abstract
In the robotic domain, it is common to deduce and use models that allow translating mathematically the element behavior. In some cases, these would serve as base to determine and develop a controller, for example. Beyond this, the simulation and experiments are reasons that leave to the development of models, becoming evaluation tools of the system behavior, especially when there are constraints of implementation or in experiments. However, the modeling is an approach to the reality, since it is difficult to translate the behavior of an element in a strict way and the disturbances to witch it is subject to. In this work, we address the modeling questions of an autonomous underwater vehicle. This paper describes the deducing of a dynamic model with six degrees of freedom of an underwater vehicle, considering all of its physical characteristics. This is achieved by the determination of all forces that actuates on the body during its motions and by the determination of the rigid body dynamic. The modeling method is presented as well as the coefficients determination. Finally, a comparison with experimental results is carried out.

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
One way to mitigate the high costs of doing science or business at sea is to create technological infrastructures possessing all the skills and resources needed for successful maritime operations, and make those capabilities and skills available to the external entities requiring them. By doing so, the individual economic and scientific agents can be spared the enormous effort of creating and maintaining their own, particular set of equivalent capabilities, thus drastically lowering their initial operating costs. In addition to cost savings, operating based on fully-fledged, shared infrastructures not only allows the use of more advanced scientific equipment and highly skilled personnel, but it also enables the business teams (be it industry or research) to focus on their goals, rather than on equipment, logistics, and support. This paper will describe the TEC4SEA infrastructure, created precisely to operate as described. This infrastructure has been under implementation in the last few years, and has now entered its operational phase. This paper will describe it, present its current portfolio of services, and discuss the most relevant assets and facilities that have been recently acquired, so that the research and industrial communities requiring the use of such assets can fully evaluate their adequacy for their own purposes and projects.