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
This paper presents an innovative modular autonomous underwater vehicle (MARA) developed for the exploration of underwater confined spaces such as underwater caves, flooded underground mines or complex tight infrastructures in underwater environments. The particular mission scenario of exploration of flooded underground mines was used as a key driver for the robot development. The autonomous underwater vehicle is described from the mechanical, hardware and software points of view. The availability of the INESC TEC underwater systems test tank and access conditions to Porto harbour and the Urgeirica mine allows for easy robot field validation. Preliminary results are also presented and discussed.

Abstract
This paper presents an autonomous robotic system, IRIS, designed for lost fishing gear recovery. The vehicle was developed in the context of the NetTag project. This is a European Union project funded by EASME the Executive Agency for Small and Medium Enterprises addressing marine litter, and the reduction of quantity and impact of lost fishing gears in the ocean. NetTag intends to produce new technological devices for location and recovery of fishing gear and educational material about marine litter, raise awareness of fisheries industry and other stakeholders about the urgent need to combat marine litter and increase scientific knowledge on marine litter problematic, guaranteeing the engagement of fishers to adopt better practices to reduce and prevent marine litter derived from fisheries. The design of IRIS is presented in detail, addressing the mechanical design, hardware architecture, sensor system and navigation and control. Preliminary tests in tank and in controlled sea conditions are presented and ongoing developments on the recovery system are discussed.

Abstract
This paper presents a high-resolution imaging system developed for plankton imaging in the context of the MarinEye integrated biological sensor [1]. This sensor aims to produce an autonomous system for marine integrated physical, chemical and biological monitoring combining imaging, acoustic, sonar, and fraction filtration systems (coupled to DNA/RNA preservation) as well as sensors for targeting physical-chemical variables in a modular and compact system that can be deployed on fixed and mobile platforms, such as the TURTLE robotic deep sea lander [2]. The results obtained with the system both in laboratory conditions and in the field are presented and discussed, allowing the characterization and validation of the performance of the Autonomous High-Resolution Image Acquisition System for Plankton.

Abstract
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area's roughness, and the spot's slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.