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Unmanned aerial vehicles (UAV), colloquially called drones, are widely applied in many sectors of the economy, including the construction industry. They are used for building inspections, damage assessment, land measurements, safety inspections, monitoring the progress of works, and others.

The study notes that UAV pose new, and not yet present, risks in the construction industry. New threats arise, among others, from the development of new technologies, as well as from the continuous automation and robotization of the construction industry. Education regarding the safe use of UAV and the proper use of drones has a chance to improve the safety of work when using these devices.

The procedure (protocol) was developed for the correct and safe preparation and planning of an unmanned aerial vehicle flight during construction operations.

Based on the analysis of available sources, no such complete procedure has yet been developed for the correct, i.e. compliant with applicable legal regulations and occupational health and safety issues, preparation for flying UAV. The verification and validation of the developed flight protocol was performed on a sample of over 100 different flight operations.

The present paper assesses the decision problem of selecting Unmanned Aerial Vehicle Systems (SARP) by the hybrid MPSI-SPOTIS approach for deployment in border control and transborder illicit combat.

By the hybrid MCDA MPSI-SPOTIS approach, and from the database available in Gettinger (2019), models were filtered by Endurance, Range, Maximum Take-Off Weight (MTOW), and Payload, fitting within the classification of Categories EB 0 and 2. Category EB 1 was not considered in this study due to the limited number of models in the data source.

The use of the Multi-Criteria Decision Analysis (MCDA) tool MPSI-SPOTIS allowed the determination of weights by stochastic criteria, applied in a ranking method resistant to reverse ordering. The application of the method identified the Raybird-3 (Cat EB 0) and Searcher (Mk3) (Cat EB 2) models as the best alternatives. From a proposed clustering, other selection possibilities with close performance in the evaluation were presented. The cost criterion was not taken into consideration due to the absence of information in the data source employed. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.

The cost criterion was not taken into consideration due to the absence of information in the data source used. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.

This paper aims to propose a technology selection method applied to complex defense acquisitions when multiple factors influence the decision makers and it is hard to obtain a major optimum solution in multitask and multi-mission platform.

This paper aims to describe the mechanical aspects of unmanned Mothership Plane and Sensing Drones. The presented conceptual system shows the idea and possible way of designing different sizes and objective systems based on experience gained during the SAE Aero Design Competition.

The UAS is based on a SAE Aero Design Competition designed and manufactured Mothership Plane converted to a high endurance platform modified to launch up to six small copters. The process of designing and converting the Mothership is described. The methodology of selecting and planning either the structure or hardware of the drones is presented.

A key finding is that the presented conception of mothership plane deploying in flight a group of small sensing multirotors is achievable. Moreover, the modular build of the system provides the possibility to adapt currently existing unmanned aircrafts to be converted to the described mothership plane.

To conduct flight tests and to study encountered problems. Presentation of the unmanned aerial system (UAS) concept that can be used to scan an area and create 3D maps for Search and Rescue missions as well as agriculture applications.

The paper describes the conceptual approach to design a UAS consisting of the mothership plane and the sensing drones. The paper highlights the potential solutions gained by using such a UAS. The focus is to present a technology and system that can perform real time observations in widespread and difficult to reach areas.

This paper aims to present the main results achieved in the frame of the TIVANO national-funded project which may anticipate, in a stepped approach, the evolution and the design of the enabling technologies needed for a hybrid/electric medium altitude long endurance (MALE) unmanned aerial vehicle (UAV) to perform persistent intelligence surveillance reconnaissance (ISR) military operations.

Different architectures of hybrid-propulsion system are analyzed pointing out their operating modes to select the more suitable architecture for the reference aircraft. The selected architecture is further analyzed together with its electric power plant branch focusing on electric system architecture and the selected electric machine. A final comparison between the hybrid and standard propulsion is given at aircraft level.

The use of hybrid propulsion may lead to a reduction of the total aircraft mass and an increase in safety level. However, this result comes together with a reduced performance in climb phase.

This study can be used as a reference for similar studies and it provides a detailed description of propulsion operating modes, power management, electric system and machine architecture.

This study presents a novel application of hybrid propulsion focusing on a three tons class MALE UAV for ISR missions. It provides new operating modes of the propulsion system and a detailed electric architecture of its powertrain branch and machine. Some considerations on noise emissions and infra-red traceability of this propulsion, at aircraft level.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

Photogrammetry enables scientists and engineers to make accurate and precise measurements from optical images and other patterns of reflected electromagnetic energy. Photogrammetry is taught in surveying, geomatics and similar academic programs. For a long time, it has been observed that there is a lack of diversity and underrepresentation of different groups in the surveying and geomatics workforces for various reasons. Diversity fosters more innovative environments, helps employees be more engaged and boosts productivity rates. Although efforts are being made to solve this problem, most attempts did not significantly improve the diversity issues in this field. To address this problem, we designed a new curriculum for a photogrammetry course, which integrates entrepreneurial mindset (EM), bio-inspired design and Science, Technology, Engineering, Arts and Mathematics (STEAM) into the photogrammetry course for this study.

In this study, the participatory action research method, Photovoice, was used to gather data. Students were asked to respond to photovoice and metacognitive reflection prompts to understand student perceptions about the importance of Unmanned Aerial Vehicles (UAVs) in photogrammetric mapping. Students were required to respond to each prompt with three pictures and a narrative. These reflections were analyzed using thematic analysis.

The analysis of the photovoice and metacognitive reflections resulted in six themes: promoting digital literacy, promoting job readiness and awareness, improving perceived learning outcomes, increasing interest in pursuing careers in surveying/geomatics, encouraging learner engagement and increasing awareness of the role of art in map making.

This is the first study conducted at our Hispanic Serving Institution, which specifically designed a curriculum integrating EM, bio-inspired design and STEAM concepts to address diversity issues in surveying and geomatics engineering disciplines.

Rapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and human–robot teams (HRTs) research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the architecture, engineering and construction (AEC) sector.

A total of 71 peer-reviewed journal articles centred on robotics and HRTs were reviewed through a quantitative approach using scientometric techniques using Gephi and VOSviewer. Research focus deductions were made through bibliometric analysis and co-occurrence analysis of reviewed publications.

This study revealed sparse and small research output in this area, indicating immense research potential. Existing clusters signifying the need for further studies are on automation in construction, human–robot teaming, safety in robotics and robotic designs. Key publication outlets and construction robotics contribution towards the built environment’s resilience are discussed.

The identified gaps in the thematic areas illustrate priorities for future research focus. It raises awareness on human factors in collaborative robots and potential design needs for construction resilience.

Rapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and HRTs research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the AEC sector.

The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones.

Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage.

Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively.

Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques.

In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage.

A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design.

The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.