Abstract
This paper addresses the use of supervised and unsupervised methods for classification of hyperspectral imaging data in maritime border surveillance domain. In this work supervised (SVM) and unsupervised (HYDADE) approaches were implemented. An evaluation benchmark was performed in order to compare methods results using real hyperspectral imaging data taken from an Unmanned Aerial Vehicle in maritime border surveillance scenario.

Abstract
Limited perception capabilities underwater shrink the envelope of effective localization techniques that can be applied in this environment. Long-term localization in six degrees of freedom can only be achieved by combining different sources of information. A multiple vehicle underwater localization solution, for localizing an underwater mining vehicle and its support vessel, is presented in this paper. The surface vessel carries a short baseline network, that interact with the inverted ultra-short baseline, carried by the underwater mining vehicle. A multiple antenna GNSS system provides data for localizing the surface vessel and to georeference the short baseline array. Localization of the mining vehicle results from a data fusion approach, that combines multiple sources of sensor information using the Extended Kalman Filter (EKF) framework. The developed solutions were applied in the context of the VAMOS! European project. Long-term real time position errors below 0.2 meters, for the underwater machine, and 0.02 meters, for the surface vessel, were accomplished in the field. All presented results are based on data acquired in a real scenario.

Abstract
This paper presents the positioning, navigation and awareness (PNA) system developed for the Underwater Robotic Mining System of the VAMOS! project [1]. It describes the main components of the VAMOS! system, the PNA sensors in each of those components, the global architecture of the PNA system, and its main subsystems: Position and Navigation, Real-time Mine Modeling, 3D Virtual reality HMI and Real-time grade system. General results and lessons learn during the first mining field trial in Lee Moor, Devon, UK during the months of September and October 2017 are presented.

Abstract
In this work an acoustic tag detector was developed for the integration in a mobile robotic fish tracking architecture. The present paper presents both the developed system and preliminary results with particular emphasis of the developed solution with the tag manufacturer receiver. The work has been developed in the context of the MYTAG Portuguese RD project, addressing the study and characterisation of the European flounder migrations in the northern estuarine environments of Portugal. The detector is to be integrated in a tracking system using autonomous surface vehicles and fixed buoys. The main objective is to detect tags inserted surgically in flounders for the MYTAG project, while simultaneously identifying them. A detector solution is presented allowing for the detection and identification of V7 VEMCO tags and preliminary comparative results with the commercially available manufacturer receivers are also presented and discussed.

Abstract
This paper presents the modeling and simulation of a spherical autonomous underwater vehicle. The robot was developed under the European Union H2020 innovation action UNEXMIN for the exploration of underground flooded mines, and is a small spherical robot with thrusters and an internal pendulum for pitch control. A model of the vehicle is presented, initially without the pendulum, then an extended formulation is derived accounting for a multibody dynamic description of the system. Experimental identification results for the determination of drag parameters are presented as well. A Modelica based simulator is developed for dynamic simulation of the vehicle, and is integrated with the Matlab/Simulink environment. The simulator is then validated based on preliminary experimental results.

Abstract
This paper describes the UX-1 underwater mine exploration robotic system under development in the context of the UNEXMIN project. UNEXMIN is an international innovation action funded under the EU H2020 program, aiming to develop new technologies and services allowing the exploration of flooded underground mines. The system is comprised by the UX-1 robot prototype, launch and recovery system, command and control subsystem and a data management and post-processing computational infrastructure. The UX-1 robot is a small spherical robot equipped with a multibeam sonar, five digital cameras and rotating laser line structured light systems. It is capable of obtaining an accurate point cloud of the surrounding environment along with high resolution imagery. A set of mineralogy, water parameters and geophysical sensors was also developed in order to obtain a more comprehensive mine model. These comprise a multi-spectral camera, electro-conductivity, pH, magnetic field sensors, a subbottom sonar, total natural gamma-ray detector, UV-light for fluorescent observation and a water sampling unit. The design of the system is presented along with the robot design. Some preliminary results are also presented and discussed