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
Autonomous underwater vehicles (AUVs) are increasingly being used to perform mine countermeasures (MCM) operations but its capabilities are limited by the efficiency of the planning process. Here we study the problem of multiobjective MCM mission planning with AUVs. The vehicle should cover the operating area while maximizing the probability of detecting the targets and minimizing the required energy and time to complete the mission. A multi-stage algorithm is proposed and evaluated. Our algorithm combines an evolutionary algorithm (EA) with a local search procedure, aiming at a more flexible and effective exploration and exploitation of the search space. An artificial neural network (ANN) model was also integrated in the evolutionary procedure to guide the search. The combination of different techniques creates another problem, related to the high amount of parameters that needs to be tuned. Thus, the effect of these parameters on the quality of the obtained Pareto Front was assessed. This allowed us to define an adaptive tuning procedure to control the parameters while the algorithm is executed. Our algorithm is compared against an implementation of a known EA as well as another mission planner and the results from the experiments show that the proposed strategy can efficiently identify a higher quality solution set. © 2014 World Scientific Publishing Company.

Abstract
FLEXUS unmanned surface vehicle was designed in the context of the Internet of Moving Things. This small catamaran weights less than 15kg and is less than 1m long, making it a very convenient vehicle with reduced logistics needs for operations in real outdoor environments. The present paper describes the resulting system both in terms of design and performances. Based on the requirements for this project, the subsystems composing the vehicle are described. Results obtained from experiments conducted in outdoor conditions have successfully validated this design and are presented in this paper.

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.