Abstract
Over the last few years, Man-Machine collaborative systems have been increasingly present in daily routines. In these systems, one operator usually controls the machine through explicit commands and assesses the information through a graphical user interface. Direct & implicit interaction between the machine and the user does not exist. This work presents a man-machine symbiotic concept & system where such implicit interaction is possible targeting search and rescue scenarios. Based on measuring physiological variables (e.g. body movement or electrocardiogram) through wearable devices, this system is capable of computing the psycho-physiological state of the human and autonomously identify abnormal situations (e.g. fall or stress). This information is injected into the control loop of the machine that can alter its behavior according to it, enabling an implicit man-machine communication mechanism. A proof of concept of this system was tested at the ARTEX (ARmy Technological EXperimentation) exercise organized by the Portuguese Army involving a military agent and a drone. During this event the soldier was equipped with a kit of wearables that could monitor several physiological variables and automatically detect a fall during a mission. This information was continuously sent to the drone that successfully identified this abnormal situation triggering the take-off and a situation awareness fly-by flight pattern, delivering a first-aid kit to the soldier in case he did not recover after a pre-determined time period. The results were very positive, proving the possibility and feasibility of a symbiotic system between humans and machines.

Abstract
Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Abstract
The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.