Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain’s topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation’s restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research’s main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions’ quality observing optimizing photometric and mission time criteria.

Unmanned Aerial Vehicles (UAVs) play a crucial role in exploring unpredictable terrains, such as accident sites and search zones. These robots require autonomous navigation and precise path planning to operate safely and efficiently in dynamic environments. UAVs must balance navigation accuracy and energy efficiency constraints during autonomous operations. This paper introduces an innovative boundary-driven mapping strategy for UAV exploration in unknown environments. The study proposes a novel approach for boundary extraction using deep learning and presents a decision-making methodology to enable the UAV to select the most optimal frontier for exploration based on deep learning-derived information. The primary objective is to efficiently expand the unknown map. The research demonstrates a significant reduction in exploration time within a simulated environment, achieving better performance compared to other methodologies documented in the literature. The results highlight the effectiveness and efficiency of the proposed strategy in terms of mapping exploration.

In recent years, the rate of urbanization has increased enormously, precipitating an escalating demand for improved services and applications in urban areas to improve the quality of life. In the Internet of Things (IoT)era, cities are transforming into smart urban centers. These cities incorporate connected devices, such as intelligent public lighting systems, to enhance their urban infrastructure. Therefore, this work explores the transformative potential of an IoT-enabled smart lighting system in urban environments, emphasizing its essential role in enhancing safety, economy, and sustainability. In this sense, LoRaCELL (Long-Range Cell) is introduced. LoRaCELL is an innovative system that utilizes edge devices for data collection, such as light intensity, humidity, temperature, air quality, solar ultraviolet radiation, ammeter, and voltmeter. It stands as a pioneering solution for intelligent public lighting systems, contributing to advancing IoT-driven urban development. The outcomes showed that the proposed system could successfully synchronize the devices with each other and send IoT sensing data at a low cost compared to traditional technologies such as LoRaWAN.