Abstract
The use of autonomous vehicles in maritime operations is a technological challenge. In the particular case of autonomous aerial vehicles (UAVs), their application ranges from inspection and surveillance of offshore power plants, and marine life observation, to search and rescue missions. Manually landing UAVs onboard water vessels can be very challenging due to limited space onboard and wave agitation. This paper proposes an autonomous solution for the task of landing commercial multicopter UAVs with onboard cameras on water vessels, based on the detection of a custom landing platform with computer vision techniques. The autonomous landing behavior was tested in real conditions, using a research vessel at sea, where the UAV was able to detect, locate, and safely land on top of the developed landing platform.

Abstract
Offshore wind farms are becoming the main alternative to fossil fuels and the future key to mitigating climate change by achieving energy sustainability. With favorable indicators in almost every environmental index, these structures operate under varying and dynamic environmental conditions, leading to efficiency losses and sudden failures. For these reasons, it's fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper introduces a new simulation environment for testing and training autonomous inspection techniques under a more realistic offshore wind farm scenario. Combining the Gazebo simulator with ROS, this framework can include multi-robots with different sensors to operate in a customizable simulation environment regarding some external elements (fog, wind, buoyancy...). The paper also presents a use case composed of a 3D LiDAR-based technique for autonomous wind turbine inspection with UAV, including point cloud clustering, model estimation, and the preliminary results under this simulation framework using a mixed environment (offshore simulation with a real UAV platform).

Abstract

Abstract
This paper presents the work performed in the implementation of an underwater simulation environment for the development of an autonomous underwater vehicle for the exploration of flooded underground tunnels. In particular, the implementation of a laser based structured light system, multibeam sonar and other robot details were addressed. The simulation was used as a relevant tool in order to study and specify the robot multiple sensors characteristics and placement in order to adequately survey a realistic environment. A detailed description of the research and development work is presented along with the analysis of obtained results and the benefits this work brings to the project.

Abstract
The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed's features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to address these tasks; however, each sensor has limitations associated with the underwater environment. This paper presents a survey of the main techniques and trends related to seabed characterization, highlighting approaches in three tasks: classification, detection, and segmentation. The bibliography is categorized into four approaches: statistics-based, classical machine learning, deep learning, and object-based image analysis. The differences between the techniques are presented, and the main challenges for deep sea research and potential directions of study are outlined.