Abstract
Autonomous Underwater Vehicles (AUV) are growing in importance for their many underwater applications, due to their characteristics and functionalities. Making use of a imaging sonar, it is possible to acquire the AUV's distance to existing obstacles. Then, through an implementation of a feature detection algorithm and an estimator, it is possible to interpolate the vehicle's relative position. This paper proposes a localization method for structured environments employing a mechanical scanning sonar feeding an extended Kalman filter. Some tests were then run in two different water tanks in order to verify the effectiveness of the solutions. These tests were performed in two different phases. For the first one, all the readings were taken with the vehicle steady and immobile. The second phase was executed with the vehicle in motion. The results are presented and compared against ground-truth measurements.

Abstract
This paper describes the work done during REX'17, an exercise conducted by the Portuguese Navy in cooperation with Portuguese universities to test, demonstrate, and develop research projects, and to approach the academic and military communities. This year the exercise took place in the Tagus river estuary, and its main aim was to assess shallow water areas, regarding bottom, and acoustic characteristics. The experiments involved testing of vector sensors development at the University of Algarve, an UUV developed by INESC-T EC, a marsupial robotic team of a USV and a hex acopter capable of landing on water developed by the New University of Lisbon (Nova), and a hydrophone network used by the Portuguese Naval Academy.

Abstract
The growing economic interest in the exploitation of mineral resources on deep-ocean beds, including those in the vicinity of sensitive-rich habitats such as hydrothermal vents, raise amounting concern about the damage that such actions might originate to these poorly-know ecosystems, which represent millions of years of evolution and adaptations to extreme environmental conditions. It has been suggested that mining may cause a major impact on vent ecosystems and other deep-sea areas. Yet, the scale and the nature of such impacts are unknown at present. Hence, building upon currently available scientific information it is crucial to develop new cost-effective technologies embedded into rigorous operating frameworks. The forward-thinking provided here will assist in the development of new technologies and tools to address the major challenges associated with deep sea-mining; technologies for in situ and ex situ observation and data acquisition, biogeochemical processes, hazard assessment of deep-sea mining to marine organisms and development of modeling tools in support of risk assessment scenarios. These technological developments are vital to validate a responsible and sustainable exploitation of the deep-sea mineral resources, based on the precautionary principle.

Abstract
This paper addresses the use of a hyperspectral image system to detect vessels in maritime operational scenarios. The developed hyperspectral imaging classification methods are based on supervised approaches and allow to detect the presence of vessels using real hyperspectral data. We implemented two different methods for comparison purposes: SVM and SAM. The SVM method, which can be considered one of most utilized methods for image classification, was implemented using linear, RBF, sigmoid and polynomial kernels with PCA for dimensionality reduction, and compared with SAM using a two classes definition, namely vessel and water. The obtained results using real data collected from a UAV allow to conclude that the SVM approach is suitable for detecting the vessel presence in the water with a precision and recall rates favorable when compared to SAM.

Abstract
This work address hyperspectral imaging systems use for maritime target detection using unmanned aerial vehicles. Specifically, by working in the creation of a hyperspectral real-time data processing system pipeline. We develop a boresight calibration method that allows to calibrate the position of the navigation sensor related to the camera imaging sensor, and improve substantially the accuracy of the target geo-reference. We also develop an unsupervised method for segmenting targets (boats) from their dominant background in real-time. We evaluated the performance of our proposed system for target detection in real-time with UAV flight data and present detection results comparing favorably our approach against other state-of- the-art method.

Abstract
Oil spill incidents in the sea or harbors occur with some regularity during exploration, production, and transport of petroleum products. In order to mitigate the impact of the oil spill in the marine life, immediate, safety, effective and ecofriendly actions must be taken. Autonomous vehicles can assume an important contribution by establishing a cooperative and coordinated intervention. This paper presents the development of a path planning control-law methods for an autonomous surface vehicle (ASV) being able to contour the oil spill while is deploying microorganisms and nutrients (bioremediation) capable of mitigating and contain the oil spill spread with the collaboration of a UAV vehicle. An oil spill simulation scenario was developed in Gazebo to support the evaluation of the cooperative actions between the ASV and UAV and to infer the ASV path planning for each one of the proposed control-law methods.