In the last few years, scattered experiences of the application of additive manufacturing in the construction of buildings using 3D printing with robots or automated equipment have emerged around the world. These use a variety of procedures and suggest relevant advantages for the construction industry. In order to identify the different processes and features in development in this field and to guide future research and applications, this article presents a review of the literature on the main aspects involved in the use of 3D printing in the construction sector. The review includes state-of-the-art material mixtures, printing technologies, and potential uses, as well as a novel analysis of building strategies, management systems, and benefits stated about this new approach for construction. It reveals progressive experimentation regarding diverse features, with challenges related to the consolidation of procedures and this technology’s readiness to participate in the building market.

The installed capacity of solar photovoltaics has increased over the past two decades worldwide, evolving from a few small scale applications to a daily power source. Such growth involves a great impact over operating processes and maintenance practices. The RGB (red, green and blue) and infra-red monitoring of photovoltaic modules is a non-invasive inspection method which provides information of possible failures, by relating thermal behaviour of the modules to the operational status of solar panels. An adequate thermal measurement module strongly depends on the proper camera angle selection relative to panel’s surface, since reflections and external radiation sources are common causes of misleading results with the unnecessary maintenance work. In this work, we test a portable ground-based system capable of detecting and classifying hot-spots related to photovoltaic module failures. The system characterizes in 3D thermal information from the panels structure to detect and classify hot-spots. Unlike traditional systems, our proposal detects false hot-spots associated with people or device reflections, and from external radiation sources. Experimental results show that the proposed diagnostic approach can provide of an adequate thermal monitoring of photovoltaic modules and improve existing methods in $12\%$ of effectiveness, with the corresponding financial impact.

The construction industry is currently technologically challenged to incorporate new developments for enhancing the process, such as the use of 3D printing for complex building structures, which is the aim of this brief. To do so, we show a systematic study regarding the usability and performance of mobile manipulators as displaceable 3D printing machinery in construction sites, with emphasis on the three main different existing mobile platforms: the car-like, the unicycle and the omnidirectional (mecanum wheeled), with an UR5 manipulator on them. To evaluate its performance, we propose the printing of the following building elements: helical, square, circular and mesh, with different sizes. As metrics, we consider the total control effort observed in the robots and the total tracking error associated with the energy consumed in the activity to get a more sustainable process. In addition, to further test our work, we constrained the robot workspace thus resembling real life construction sites. In general, the statistical results show that the omnidirectional platform presents the best results –lowest tracking error and lowest control effort– for circular, helicoidal and mesh building elements; and car-like platform shows the best results for square-like building element. Then, an innovative performance analysis is achieved for the printing of building elements, with a contribution to the reduction of energy consumption.

Intelligent transportation and advanced mobility techniques focus on helping operators to efficiently manage navigation tasks in smart cities, enhancing cost efficiency, increasing security, and reducing costs. Although this field has seen significant advances in developing large-scale monitoring of smart cities, several challenges persist concerning the practical assignment of delivery personnel to customer orders. To address this issue, we propose an architecture to optimize the task assignment problem for delivery personnel. We propose the use of different cost functions obtained with deterministic and machine learning techniques. In particular, we compared the performance of linear and polynomial regression methods to construct different cost functions represented by matrices with orders and delivery people information. Then, we applied the Hungarian optimization algorithm to solve the assignment problem, which optimally assigns delivery personnel and orders. The results demonstrate that when used to estimate distance information, linear regression can reduce estimation errors by up to 568.52 km (1.51%) for our dataset compared to other methods. In contrast, polynomial regression proves effective in constructing a superior cost function based on time information, reducing estimation errors by up to 17,143.41 min (11.59%) compared to alternative methods. The proposed approach aims to enhance delivery personnel allocation within the delivery sector, thereby optimizing the efficiency of this process.

The knowledge of the workspace for a robotic system on construction sites represents an essential resource to ensure the work progress, guarantee the safety of the construction tasks, and avoid robot damage. Despite the dramatic development of 3D printing technologies with robotic systems in recent years, these are still several challenges to consider, such as the size of the printing profile and obstacles in the construction site. This work presents the results from evaluating the workspace of a mobile manipulator in 3D printing tasks on construction sites. The methodology analyses the printing workspace based on the workspace of the mobile manipulator, considering fixed obstacles and possible collisions between the robot and obstacles during 3D printing tasks. The results showed that the shape of the printing profile defined as a building element changes the shape of the printing workspace. Furthermore, the obstacles in the construction site and height variation of the printing profile cause changes in the displacement of the robotic platform and values of rotation of its joints, which also modify the shape of the printing workspace.

Electric field energy harvesters (EFEHs) are reliable and sustainable power sources that may be used to power wireless sensor nodes (WSNs) in urban Internet of Things (IoT) networks, replacing traditional batteries. Despite the technology’s effectiveness, large-scale deployments raise severe environmental concerns about fabrication material degradation and recycling. In this context, this work analyzes the performance of the natural green leaves as a replacement for standard electrodes in EFEH developments. To this end, different $10\times 3$ cm ² EFEHs were assembled with raw leaves from the following species Magnolia Obovata, Ravenala Madagascariensis, Acanthus Mollis, and Agapanthus Africanus. Each harvester was evaluated at different drying steps, concluding that natural leaves may collect electrostatic charges related to the urban electric field, which might power ultra-low-energy devices. Experimental results reveal that Agapanthus Africanus electrodes perform best, with an open-circuit voltage (VOC) of 111.88 V and a short-circuit current (ISC) of 229.09 nA. On the other hand, the VOC of Magnolia Obavata leaves achieved 76.76 V, and the ISC was 135.30 nA (the worst case). Although the performance for Ravenala Madagascariensis samples is less than Agapanthus Africanus ones, the size leaf is another critical parameter to design functional devices. Therefore, the experimental section also includes the conceptualization, design, and experimental testing of a functional EFEH prototype called Leaf-EFEH, which is assembled with Ravenala Madagascariensis leaves. Finally, numerous experiments have shown that the proposed Leaf-EFEH can power ultra-low-power devices.