Abstract
Fishing for deep-sea species occurs on continental slopes, ridges, and seamounts. Fishing operations using fishing gears that contact the bottom (e.g., trawls and bottom longlines) may have significant impacts on Vulnerable Marine Ecosystems (VMEs). VMEs refer to marine ecosystems with a population or community of sensitive taxa or habitats that are likely to experience substantial alteration from short-term to chronic disturbance and that are unlikely to recover during the timeframe in which the disturbance occurs. The VME concept, introduced in the United Nations General Assembly Resolution 61/105, has been worldwide applied to the management of deep-sea fisheries. However, the effective identification and management of VMEs is highly constrained by the scarcity of data on VME indicator taxa. This data deficiency is usually surpassed by the use of VME predictive modelling. Video footage is a non-destructive method commonly used for exploring and investigating areas of seabed and for characterising and identifying habitat types. Remotely Operated Vehicles (ROVs) are one of the tools for seabed mapping. ROVs range in size from small observation-class to large work-class vehicles. Their sizes determine the payload, manoeuvrability, depth rating and ultimately uses of the vehicle. For epifaunal imaging, ROVs can be used in two modes: qualitative inspections and quantitative assessments. This paper presents the development of an innovative system composed of a compact support research vessel and a hybrid autonomous underwater vehicle capable of accurate georeferenced high-resolution imaging and profiling of the seabed for a detailed survey of the seabed for biodiversity studies. The experimental results obtained by the developed system in field work in real VME survey at 600m depth are presented.

Abstract
The increasing development of Unmanned Surface Vehicles (USVs) for various applications in open and shallow waters has increased demand for more advanced USVs with improved safety and navigation systems. This article introduces a collision avoidance system for USVs that complies with the International Regulations for Preventing Collisions at Sea (COLREG) rules, particularly rules 13 to 18 from Part B - Steering and Sailing. The system utilizes a three-block architecture for risk assessment, situation identification, and path replanning. Practical testing and validation were conducted using the Stonefish simulator, demonstrating the system's effectiveness in ensuring compliance with COLREG rules and facilitating safe navigation of USVs.

Abstract
The eDNA autonomous biosampler results from a line of research aimed at developing systems for sampling and collecting marine biological data, and for collecting environmental DNA. Environmental DNA is a tool that has been increasingly used in the biological monitoring of aquatic environments, as it is a non-invasive method with very promising results when it comes to assessing biological diversity. In this sense, the automation of this method has the potential to greatly increase the temporal and spatial resolution of current biological monitoring programs in aquatic environments. The system has been developed in a partnership between research teams at the Centre for Robotics and Autonomous Systems (CRAS - INESC TEC) and CIIMAR and has been tested in multiple operational scenarios, including the Arctic, where it was attached to the AUV IRIS.

Abstract
This paper presents the Unmanned Aerial Vehicle (UAV) MANTIS, developed for indoor inventory management in large-scale warehouses. MANTIS integrates a visual odometry (VIO) system for precise localization, thus allowing indoor navigation in complex environments. The mechanical design was optimized for stability and maneuverability in confined spaces, incorporating a lightweight frame and efficient propulsion system. The UAV is equipped with an array of sensors, including a 2D LiDAR, six cameras, and two IMUs, which ensures accurate data collection. The VIO system integrates visual data with inertial measurements to maintain robust, drift-free localization. A behavior tree (BT) framework is responsible for the UAV mission planner assigned to the vehicle, which can be flexible and adaptive in response to dynamic warehouse conditions. To validate the accuracy and reliability of the VIO system, we conducted a series of tests using an OptiTrack motion capture system as a ground truth reference. Comparative analysis between the VIO and OptiTrack data demonstrates the efficacy of the VIO system in maintaining accurate localization. The results prove MANTIS, with the required payload sensors, is a viable solution for efficient and autonomous inventory management.

Abstract
Unmanned Aerial Vehicle (UAV) applications, particularly for indoor tasks such as inventory management, infrastructure inspection, and emergency response, are becoming increasingly complex with dynamic environments and their different elements. During operation, the vehicle's response depends on various decisions regarding its surroundings and the task goal. Reinforcement Learning techniques can solve this decision problem by helping build more reactive, adaptive, and efficient navigation operations. This paper presents a framework to simulate the navigation of a UAV in an operational environment, training and testing it with reinforcement learning models for further deployment in the real drone. With the support of the 3D simulator Gazebo and the framework Robot Operating System (ROS), we developed a training environment conceivably simple and fast or more complex and dynamic, explicit as the real-world scenario. The multi-environment simulation runs in parallel with the Deep Reinforcement Learning (DRL) algorithm to provide feedback for the training. TD3, DDPG, PPO, and PPO+LSTM were trained to validate the framework training, testing, and deployment in an indoor scenario.

Abstract
Involving people in urban planning offers many benefits, but current methods are failing to get a large number of citizens to participate. People have a high participation barrier when it comes to public participation in urban planning - as it requires a lot of time and initiative, only a small non-diverse group of citizens take part in governmental initiatives. In this paper, a product is developed to make it as easy as possible for citizens to get involved in construction projects in their community at an early stage. As a solution, a public screen is proposed, which offers citizens the opportunity to receive information, view 3D models, vote and comment at the site of the construction project via smartphone - the solution was named Parcitypate. To explain the functions of the product, a prototype was created and tested. In addition, concepts for branding, marketing, ethics, and sustainability are presented.