Abstract
Zooplankton plays a key -role on Earth's ecosystem, emerging in the oceans and rivers in great quantities and diversity, making it an important and rather common topic on scientific studies. Given the numbers of different species it is not only necessary to study their numbers but also their classification. In this paper a possible solution for the zooplankton in situ detection and classification problem in real-time is proposed using a portable deep learning approach based on Convolutional Neural Networks deployed on INESC TEC's MarinEye system. For detection a Single Shot Detection model with MobileNet was used, and ZooplanktoNet for classification. System portability is guaranteed with the use of MovidiusTMNeural Compute Stick as the deep learning motor.

Abstract
For the context of a mobile tracking system, an underwater acoustic positioning system was developed, using three hydrophones to compute the direction of an acoustic source relative to an Autonomous Surface Vehicle (ASV). The paper presents an algorithm for the Direction of Arrival (DoA) of an acoustic source, which allows to estimate its position. Preliminary results will be shown in this paper relative to the detection and identification (ID) of the acoustic sources, as well as an analysis of the proposed algorithm. The solution allows the position estimation of an acoustic source, which can be used in tracking solutions. The system can be applied in an ASV or fixed buoys, as long as the baseline's hydrophones are at equal angular distances. The main objective is to track targets with the DoA algorithm as well to estimate their position, improving what was done in [1].

Abstract
Over the last few years, oil spill incidents occured with some regularity during exploration, production and transportation, causing a large economic and ecologic impact in the world community. To minimise these impacts and reduce the time response of the initial mitigation process, autonomous vehicles, such as unmanned aerial vehicles (UAV) can be used to perform oil spill monitoring and mitigation. This paper presents the design and implementation of a multirotor UAV capable of identifying and combat the oil spill, by using a release system of consortia with bacteria and nutrients. Several field tests occurred in Portugal and Spain, where the oil spill was implemented in a simulated environment, resulting in a cooperative and simultaneous manoeuvre between the vehicles.

Abstract
The overall aim of the ROSM project is the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). These solutions will be based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in-situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be used as the first line of the responder to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or during transport activities, in a fast, efficient and low-cost way. The paper will address the development of a team of autonomous vehicles able to carry, as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), the development of a multi-robot system able to provide a first line responses to oil spill incidents under unfavourable and harsh conditions with low human intervention, and then a decentralized cooperative planning with the ability to coordinate an efficient oil spill combat. Field tests have been performed in Leixoes Harbour in Porto and Medas, Portugal, with a simulated oil spill and validated the decentralized coordinated task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV).

Abstract
Autonomous Surface Vehicles (ASVs), operating near ship harbors or relatively close to shorelines must be able to steer away from incoming vessels and other possible obstacles, be they dynamic or not. To do this, one must implement some type of multi-target tracking and obstacle avoidance algorithms that lets the vehicle dodge obstacles. This paper presents a radar-based multi-target tracking system developed for obstacle detection in a small unmanned surface vehicle. The system was designed for ROAZ II ASV belonging to INESC TEC/ISEP and implemented in Robot Operating System (ROS) for easier integration with the already existing software.

Abstract
The exploration of water bodies from the sea to land filled water spaces has seen a continuous increase with new technologies such as robotics. Underwater images is one of the main sensor resources used but suffer from added problems due to the environment. Multiple methods and techniques have provided a way to correct the color, clear the poor quality and enhance the features. In this paper, we present the work of an Image Cleaning and Enhancement Technique which is based on performing color correction on images incorporated with Dark Channel Prior(DCP) and then taking the converted images and modifying them into the Long, Medium and Short(LMS) color space, as this space is the region in which the human eye perceives colour. This work is being developed at INESC TEC robotics and autonomous systems laboratory. Our objective is to improve the quality of images for and taken by robots with the particular emphasis on underwater flooded mines. The paper describes the architecture and the developed solution. A comparative analysis with state of the art methods and of our proposed solution is presented. Results from missions taken by the robot in operational mine scenarios are presented and discussed and allowing for the solution characterization and validation.