Abstract
Navigation is one of the most important tasks for a mobile robot and the localisation is one of its main requirements. There are several types of localisation solutions such as LiDAR, Radio-frequency and acoustic among others. The well-known line follower has been a solution used for a long time ago and still remains its application, especially in competitions for young researchers that should be captivated to the scientific and technological areas. This paper describes two methodologies to estimate the position of a robot placed on a gradient line and compares them. The Least Squares and the Machine Learning methods are used and the results applied to a real robot allow to validate the proposed approach.

Abstract
This work presents a methodology for characterizing ultrasonic and LASER sensors aimed at detecting obstacles within the context of electrical inspections by multirotor Unmanned Aerial Vehicles (UAVs). A set of four ultrasonic and LASER sensor models is evaluated against eight target components, typically found in high-voltage towers. The results show that ultrasonic sensor arrays displaced 25. apart reduce the chances of problems related to crosstalk and angular uncertainty. Within the LASER sensor suite, solar exposure directly affects the detection behavior among lower power sensors. Based on the results obtained, a set of sensors capable of detecting multiple obstacles belonging to a high-voltage tower was identified. In this reasoning, it becomes possible to model sensor architectures for multirotor UAVs to detect multiple obstacles and advance in the state of the art in obstacle avoidance systems by UAVs in inspections of high-voltage towers.

Abstract
The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms' ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology's performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

Abstract
The world wine sector is a multi-billion dollar industry with a wide range of economic activities. Therefore, it becomes crucial to monitor the grapevine because it allows a more accurate estimation of the yield and ensures a high-quality end product. The most common way of monitoring the grapevine is through the leaves (preventive way) since the leaves first manifest biophysical lesions. However, this does not exclude the possibility of biophysical lesions manifesting in the grape berries. Thus, this work presents three pre-trained YOLO models (YOLOv5x6, YOLOv7-E6E, and YOLOR-CSP-X) to detect and classify grape bunches as healthy or damaged by the number of berries with biophysical lesions. Two datasets were created and made publicly available with original images and manual annotations to identify the complexity between detection (bunches) and classification (healthy or damaged) tasks. The datasets use the same 10,010 images with different classes. The Grapevine Bunch Detection Dataset uses the Bunch class, and The Grapevine Bunch Condition Detection Dataset uses the OptimalBunch and DamagedBunch classes. Regarding the three models trained for grape bunches detection, they obtained promising results, highlighting YOLOv7 with 77% of mAP and 94% of the F1-score. In the case of the task of detection and identification of the state of grape bunches, the three models obtained similar results, with YOLOv5 achieving the best ones with an mAP of 72% and an F1-score of 92%.

Abstract
This research proposes positioning obstacle detection sensors by multirotor unmanned aerial vehicles (UAVs) dedicated to detailed inspections in high voltage towers. Different obstacle detection sensors are analyzed to compose a multisensory architecture in a multirotor UAV. The representation of the beam pattern of the sensors is modeled in the CoppeliaSim simulator to analyze the sensors' coverage and detection performance in simulation. A multirotor UAV is designed to carry the same sensor architecture modeled in the simulation. The aircraft is used to perform flights over a deactivated electrical tower, aiming to evaluate the detection performance of the sensory architecture embedded in the aircraft. The results obtained in the simulation were compared with those obtained in a real scenario of electrical inspections. The proposed method achieved its goals as a mechanism to early evaluate the detection capability of different previously characterized sensor architectures used in multirotor UAV for electrical inspections.

Abstract
Life in society has initiated a search for comfort and security in social centers. This search generated revolutions within the knowledge about the technologies involved, making the environments automated and integrated. Along with this increase, ecological concerns have also arisen, which have been involved since the design of intelligent buildings, remaining through the years of their use. Based on these two pillars, the present study aims to monitor three central systems inside the apartments of the Apolo Building (Braganca city, Portugal). The electrical energy consumption, water flow, and waste disposal systems are integrated through a single database. The data is sent remotely via WiFi through the microcontroller. For better visualization and analytics of the data, a web application is also developed, which allows for real-time monitoring. The obtained results demonstrate to the consumer his behavior regarding household expenses. The idea of showing the consumer their expenditure is to create an ecological awareness. Through the data collected and the environmental alternatives found, it is possible to observe whether there was a behavior change when receiving this data, either in the short or long term.