Abstract
This paper describes the implementation of a navigation algorithm for Autonomous Surface Vehicles (ASVs), that is composed by two stages: 1) spline curve follower and; 2) reactive collision avoidance, obeying to the International Regulations for Preventing Collisions at Sea (COLREGs). The spline curve follower determines path's parametric functions that the vehicle should follow, taking into account : 1) the initial and goal points on the fixed world frame and; 2) the final desired orientation for the ASV. The reactive collision avoidance substitutes the splines navigation in situations of potential collision with moving obstacles. To do this, the algorithm considers the relative velocity between the controlled ASV and the moving obstacle (other ASV). It also takes into account the escape trajectory that the controlled ASV is capable to perform at each instant. The algorithm was implemented under the Robotic Operating System (ROS) framework. An intuitive spline curve configuration tool, using the RVIZ's package. The paper presents results of the simulation of two ASVs, following predefined spline trajectories, and the reactive collision avoidance routine in a rendezvous situation. A reference for a video illustrating the navigation algorithm is also provided.

Abstract
In opposition to the surface, no common solution is available for localization of active objects underwater. Typical solutions use acoustics as a means to implicitly measure ranges or angles and consequently determine the position of a transmitter. If the receivers are synchronized among themselves, the position of the transmitter can be estimated based on the time-of-arrivals (TOA). The confidence on the estimate varies with respect to the relative positions of the receivers and the transmitter. In this paper, we present recent developments for optimal 3D positioning of TOA sensors based on the a metric that uses the Fisher information matrix. We give the necessary conditions to obtain the best possible estimate. To our best knowledge, no analytical solution has been yet presented for this problem. We complete and validate our study with a simulation of optimal positioning of four TOA sensors.

Abstract
Today there are different teams specializing in different areas such as shipwrecked rescue, searching for mines, environmental monitoring, border surveillance, traffic control, search and rescue and harbor protecting. Robotic systems and unmanned vehicles can provide additional capabilities and new innovative solutions that contribute to these applications. This paper presents the Robotic Exercises 2014 (REX'14) and the lessons learned with various field experiments performed with multiple unnamed systems in the context of the Portuguese Navy concept of operations. During the REX'2014 multiple experiments and systems were operated. Autonomy and environment characterization and assessment missions were performed with autonomous surface vehicles such as the ROAZ autonomous surface vehicle or with autonomous underwater vehicle MARES. Autonomy and system validation was performed for fast water jet propelled surface systems such as the SWIFT autonomous surface vehicle and the ICARUS unmanned rescue capsule, wind propulsion tests were also performed with unnamed surface vehicles and new maritime wireless communication protocols were tested.

Abstract
INESC TEC is strongly committed to become a center of excellence in maritime technology and, in particular, deep sea technology. The STRONGMAR project aims at creating solid and productive links in the global field of marine science and technology between INESC TEC and established leading research European institutions, capable of enhancing the scientific and technological capacity of INESC TEC and linked institutions, helping raising its staff's research profile and its recognition as a European maritime research center of excellence. The STRONGMAR project seeks complementarity to the TEC4SEA research infrastructure: on the one hand, TEC4SEA promotes the establishment of a unique infrastructure of research and technological development, and on the other, the STRONGMAR project intends to develop the scientific expertise of the research team of INESC TEC.

Abstract
Traditional coverage path planners create lawnmower-type paths in the operating area completely ignoring the uncertainty in the vehicle's position. However, in the presence of significant uncertainty in localization estimates, one can no longer guarantee that the vehicle will cover all the area according to plan. Aiming to bridge this gap, we present a coverage path planning technique for search operations which takes into account the vehicle's position and detection performance uncertainties and tries to minimize this uncertainty along the planned path. The objective is to plan paths, using a localization error model as input, to reduce as much uncertainty as possible and to minimize the extra path length (swath overlap) while satisfying mission feasibility constraints. We introduce an algorithm that calculates what will be the best moments for bringing the vehicle to surface to ensure a bounded position error. We also consider time and energy constraints that may influence the planned trajectory as path overlap is increased to account for uncertainty. Additionally we challenge the assumption frequently seen in coverage algorithms where two observations of the same target are considered independent. © 2017 IEEE.

Abstract
Underwater imaging is being increasingly helpful for the autonomous robots to reconstruct and map the marine environments which is fundamental for searching for pipelines or wreckages in depth waters. In this context, the accuracy of the information obtained from the environment is of extremely importance. This work presents a study about the accuracy of a reconfigurable stereo vision system while determining a dense disparity estimation for underwater imaging. The idea is to explore the advantage of this kind of system for underwater autonomous vehicles (AUV) since varying parameters like the baseline and the pose of the cameras make possible to extract accurate 3D information at different distances between the AUV and the scene. Therefore, the impact of these parameters is analyzed using a metric error of the point cloud acquired by a stereoscopic system. Furthermore, results obtained directly from an underwater environment proved that a reconfigurable stereo system can have some advantages for autonomous vehicles since, in some trials, the error was reduced by 0.05m for distances between 1.125 and 2.675 m.