Abstract
By creating a dependable, transparent, and cost-effective system for forecasting and ongoing environmental impact monitoring of exploration and exploitation activities in the deep sea, TRIDENT seeks to contribute to the sustainable exploitation of seabed mineral resources. In order to operate autonomously in remote locations under harsh conditions and send real-time data to authorities in charge of granting licenses and providing oversight, this system will create and integrate new technology and innovative solutions. The efficient monitoring and inspection system that will be created will abide by national and international legal frameworks. At the sea surface, mid-water, and the bottom, TRIDENT will identify all pertinent physical, chemical, geological, and biological characteristics that must be monitored. It will also look for data gaps and suggest procedures for addressing them. These are crucial actions to take in order to produce accurate indicators of excellent environmental status, statistically robust environmental baselines, and thresholds for significant impact, allowing for the standardization of methods and tools. In order to monitor environmental parameters on mining and reference areas at representative spatial and temporal scales, the project consortium will thereafter develop and test an integrated system of stationary and mobile observatory platforms outfitted with the most recent automatic sensors and samplers. The system will incorporate high-capacity data processing pipelines able to gather, transmit, process, and display monitoring data in close to real-time to facilitate prompt actions for preventing major harm to the environment. Last but not least, it will offer systemic and technological solutions for predicting probable impacts of applying the developed monitoring and mitigation techniques.

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
For Vertical Take-Off and Landing Unmanned Aerial Vehicles (VTOL UAVs) to operate autonomously and effectively, it is mandatory to endow them with precise landing abilities. The UAV has to be able to detect the landing target and to perform the landing maneuver without compromising its own safety and the integrity of its surroundings. However, current UAVs do not present the required robustness and reliability for precise landing in highly demanding scenarios, particularly due to their inadequacy to perform accordingly under challenging lighting and weather conditions, including in day and night operations.This work proposes a multimodal fiducial marker, named ArTuga (Augmented Reality Tag for Unmanned vision-Guided Aircraft), capable of being detected by an heterogeneous perception system for accurate and precise landing in challenging environments and daylight conditions. This research combines photometric and radiometric information by proposing a real-time multimodal fusion technique that ensures a robust and reliable detection of the landing target in severe environments.Experimental results using a real multicopter UAV show that the system was able to detect the proposed marker in adverse conditions (such as at different heights, with intense sunlight and in dark environments). The obtained average accuracy for position estimation at 1 m height was of 0.0060 m with a standard deviation of 0.0003 m. Precise landing tests obtained an average deviation of 0.027 m from the proposed marker, with a standard deviation of 0.026 m. These results demonstrate the relevance of the proposed system for the precise landing in adverse conditions, such as in day and night operations with harsh weather conditions.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Abstract
A perception module is a vital component of a modern robotic system. Vision, radar, thermal, and LiDAR are the most common choices of sensors for environmental awareness. Relying on singular sources of information is prone to be affected by specific environmental conditions (e.g., visual cameras are affected by glary or dark environments). Thus, relying on different sensors is an essential step to introduce robustness against various environmental conditions. Hence, a perception system with sensor fusion capabilities produces the desired redundant and reliable awareness critical for real-world systems. This paper proposes a novel early fusion module that is reliable against individual cases of sensor failure when detecting an offshore maritime platform for UAV landing. The model explores the early fusion of a still unexplored combination of visual, infrared, and LiDAR modalities. The contribution is described by suggesting a simple methodology that intends to facilitate the training and inference of a lightweight state-of-the-art object detector. The early fusion based detector achieves solid detection recalls up to 99% for all cases of sensor failure and extreme weather conditions such as glary, dark, and foggy scenarios in fair real-time inference duration below 6 ms.

Abstract
The use of Unmanned Aerial Vehicles (UAVs) in different inspection tasks is increasing. This technology reduces inspection costs and collects high quality data of distinct structures, including areas that are not easily accessible by human operators. However, the reduced energy available on the UAVs limits their flight endurance. To increase the autonomy of a single flight, it is important to optimize the path to be performed by the UAV, in terms of energy loss. Therefore, this work presents a novel formulation of the Travelling Salesman Problem (TSP) and a path planning algorithm that uses a UAV energy model to solve this optimization problem. The novel TSP formulation is defined as Asymmetric Travelling Salesman Problem with Precedence Loss (ATSP-PL), where the cost of moving the UAV depends on the previous position. The energy model relates each UAV movement with its energy consumption, while the path planning algorithm is focused on minimizing the energy loss of the UAV, ensuring that the structure is fully covered. The developed algorithm was tested in both simulated and real scenarios. The simulated experiments were performed with realistic models of wind turbines and a UAV, whereas the real experiments were performed with a real UAV and an illumination tower. The inspection paths generated presented improvements over 24% and 8%, when compared with other methods, for the simulated and real experiments, respectively, optimizing the energy consumption of the UAV.