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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.

This study focuses on the selection of armed unmanned aerial vehicles (AUAV), which have recently taken an important place on the world agenda, are used effectively in the defense industry and change the war systems of countries. This study aims to select the most suitable armed AUAV by using and comparing multi-criteria decision-making (MCDM) methods.

There are various types of (unmanned aerial vehicles) UAVs, and some of them are Armed UAVs. This study used the criteria obtained from the market and previous UAV studies and ranked/selected various AUAVs produced in line with the determined criteria. The AHP method was used to prioritize the criteria, and the PROMETHEE method, a powerful ranking method, was used to rank/select the alternatives.

By the expert judgments, the payload capacity (28.2%) criteria took first rank by far as the most important criteria. The AUAV alternatives are listed as 1-6-5-2-7-3-4, respectively.

UAVs around the world have been showing significant and rapid developments recently, and those concerned closely follow developments in this field. Depending on the development of the aviation industry and technology, UAVs provide services to individuals or institutions in various ways for civil or military use.

The difference from similar studies is the research of Armed UAVs. Sensitivity analysis was performed and alternatives were analyzed by their weights. Comparisons were made using the MEREC, LOPCOW, and ELECTRE methods.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

Unmanned aerial vehicles (UAVs) are more affected by adverse wind conditions in especially landing. Therefore, they need to change the runway in use. In case of this change, to eliminate the uncertain maneuvers, there is a need for a special prescribed track. The purpose of this study is the construction of a prescribed track at a single runway to provide a facility to change the runway in use.

Two forms of prescribed tracks, as standard and alternate, were constructed for UAVs by taking into consideration the key parameters to design flight procedures. Both tracks were assessed in a real-time simulation method. Moreover, unmanned vehicle simulation was used for a validation process.

According to the real-time simulation results, 8.14 NM and 6.64 NM of flight distance and 5.43 min and 4.43 min of flight time for the standard and alternate prescribed tracks were found, respectively. The obtained results were in favor of the alternate prescribed track. Furthermore, the prescribed track was assessed and validated in both air traffic control and UAV simulations. The feedback of pilots and controllers was very positive for a prescribed track, as it provided them with foresight and time to take care in any situations.

The prescribed track in this paper may be applied by airspace designers and UAV users to perform safe and efficient landing in adverse wind conditions.

In this study, a prescribed track was constructed for UAVs. Quantitative results were achieved using a real-time simulation method in terms of flight distance and flight time. Additionally, validation of the prescribed track was achieved by unmanned air vehicle simulation.

The paper aims to investigate the compatibility of manned-aircraft airborne collision avoidance systems (ACAS) for use on unmanned aerial vehicles (UAV).

The paper uses the Fault Tree method for defining ACAS model adapted for the UAV operations, with the aim of showing the presence of certain factors that configure in such ACAS system, and whose failure can lead to an adverse event – mid-air collision.

Based on the effectiveness analysis of ACAS solution adapted for the UAV operations, for given inputs, it can be concluded that the probability of ACAS failure is on the order of 10–4, as well as that in the case of autonomous ACAS solution for the UAV, the probability is reduced to 10–5. The most influential factors for the failure of the UAV’s ACAS are as follows: technical implications on the UAV, human factor, sensor error, communication and C2 link issue.

The established model could be used both in the UAV’s ACAS design and application phases, with the aim of assessing the effectiveness of the adopted solution. The model outputs not only highlight the critical points of the system but also provide the basis for defining the Target Level of Safety (TLOS) for the UAV operations.

The developed model can be expected to speed up the design and adoption process of ACAS solutions for the UAVs. Also, the paper presents one of the first attempts to quantify TLOS for the UAV operations in the context of collision avoidance systems.

To avoid this situation, the authors proposed an optimal artificial bee colony algorithm-based Unmanned Aerial Vehicle (UAV) routing algorithm for efficient data communication between doctors and patients. This proposed method worked in three stages.

In recent decades, wireless body area networks have played an important role in health care applications. It facilitates the transmission of the patients' health data analysis report to the appropriate doctors.

In the first phase, biological sensors are connected to the human body via a controller node and collected data is transmitted via Bluetooth to the Personal Device Assistant (PDA). In the second phase, collected data will be transmitted via the Internet of things using an artificial bee colony algorithm. The second aids in determining the best route. In the third phase, unmanned aerial vehicles will use the best path to send collected data to doctors, caregivers, ambulances and cloud storage servers.

The simulation results show that the network's performance is superior when compared to existing approaches. The proposed algorithm achieves a high throughput, a lower delay, a higher link rate and a higher delivery rate.

A hybrid-electric unmanned aerial vehicle (HE-UAV) model has been developed to address the problem of low endurance of a small electric UAV. Electric-powered UAVs are not capable of achieving a high range and endurance due to the low energy density of its batteries. Alternatively, conventional UAVs (cUAVs) using fuel with an internal combustion engine (ICE) produces more noise and thermal signatures which is undesirable, especially if the air vehicle is required to patrol at low altitudes and remain undetected by ground patrols. This paper aims to investigate the impact of implementing hybrid propulsion technology to improve on the endurance of the UAV (based on a 13.6 kg UAV).

A HE-UAV model is developed to analyze the fuel consumption of the UAV for given mission profiles which were then compared to a cUAV. Although, this UAV size was used as reference case study, it can potentially be used to analyze the fuel consumption of any fixed wing UAV of similar take-off weight. The model was developed in a Matlab-Simulink environment using Simulink built-in functionalities, including all the subsystem of the hybrid powertrain. That is, the ICE, electric motor, battery, DC-DC converter, fuel system and propeller system as well as the aerodynamic system of the UAV. In addition, a ruled-based supervisory controlled strategy was implemented to characterize the split between the two propulsive components (ICE and electric motor) during the UAV mission. Finally, an electrification scheme was implemented to account for the hybridization of the UAV during certain stages of flight. The electrification scheme was then varied by changing the time duration of the UAV during certain stages of flight.

Based on simulation, it was observed a HE-UAV could achieve a fuel saving of 33% compared to the cUAV. A validation study showed a predicted improved fuel consumption of 9.5% for the Aerosonde UAV.

The novelty of this work comes with the implementation of a rule-based supervisory controller to characterize the split between the two propulsive components during the UAV mission. Also, the model was created by considering steady flight during cruise, but not during the climb and descend segment of the mission.

Fully-actuated unmanned aerial vehicles (UAVs) are a growing and promising field of research, which shows advantages for aerial physical interaction. The purpose of this paper is to construct a force sensor-denied control method for a fully-actuated hexarotor to conduct aerial interaction with accurate force exerted outward.

First, by extending single-dimension impedance model to the fully-actuated UAV model, an impedance controller is designed for compliant UAV pose/force control. Then, to estimate the interaction force between UAV end-effector and external environment accurately, combined with super-twisting theory, a nonlinear force observer is constructed. Finally, based on impedance controller and estimated force from observer, an interaction force regulation method is proposed.

The presented nonlinear observer-based impedance control approach is validated in both simulation and environments, in which the authors try to use a fully-actuated hexarotor to accomplish the task of aerial physical interaction finding that a specified force is able to be exerted to environment without any information from force sensors.

A solution of aerial physical interaction for UAV system enabling accurate force exerted outward without any force sensors is proposed in this paper.

The purpose of the paper is to describe the evolving regulatory structures of the civilian unmanned aerial vehicle (UAV) in India and Japan, not yet fully developed to regulate the deployment of the UAV. India and Japan are at the forefront to overhaul the respective regulatory framework to address issues of accountability, responsibility and risks associated with the deployment of UAV technologies.

In-depth interviews are conducted both in Japan and India to gather primary data based on the snowball sampling method. The paper addresses questions such as what is the current scenario of civilian UAV deployment in India and Japan. What are the regulation structures for Civil UAV deployment and operation and how they differ in India and Japan? What are the key regulatory challenges for Civil UAV deployment in India? How regulation structure enables or inhibits the users and operators of Civil UAVs in India? What are mutual learnings concerning UAV regulations?

Findings reveal that the Indian regulations address issues of responsibility by imparting values of privacy, safety, autonomy and security; Japanese regulation prefers values of trust, responsibility, safety and ownership with more freedom to experiment.

The study on civilian UAV regulatory framework is a new and innovative work embedded by the dimensions of responsibility and accountability from a responsible innovation perspective. The work is a new contribution to innovation literature looked at from regulatory structures. Field visits to both Japan and India enrich the study to a new elevation.

This paper aims to provide a mathematical modeling and design of H-infinity controller for an autonomous vertical take-off and landing (VTOL) Quad Tiltrotor hybrid unmanned aerial vehicles (UAVs). The variation in the aerodynamics and model dynamics of these aerial vehicles due to its tilting rotors are the key issues and challenges, which attracts the attention of many researchers. They carry parametric uncertainties (such as non-linear friction force, backlash, etc.), which drives the designed controller based on the nominal model to instability or performance degradation. The controller needs to take these factors into consideration and still give good stability and performance. Hence, a robust H-infinity controller is proposed that can handle these uncertainties.

A unique VTOL Quad Tiltrotor hybrid UAV, which operates in three flight modes, is mathematically modeled using Newton–Euler equations of motion. The contribution of the model is its ability to combine high-speed level flight, VTOL and transition between these two phases. The transition involves the tilting of the proprotors from 90° to 0° and vice-versa in 15° intervals. A robust H-infinity control strategy is proposed, evaluated and analyzed through simulation to control the flight dynamics for different modes of operation.

The main contribution of this research is the mathematical modeling of three flight modes (vertical takeoff–forward, transition–cruise-back, transition-vertical landing) of operation by controlling the revolutions per minute and tilt angles, which are independent of each other. An autonomous flight control system using a robust H-infinity controller to stabilize the mode of transition is designed for the Quad Tiltrotor UAV in the presence of uncertainties, noise and disturbances using MATLAB/SIMULINK. This paper focused on improving the disturbance rejection properties of the proposed UAV by designing a robust H-infinity controller for position and orientation trajectory regulation in the presence of uncertainty. The simulation results show that the Tiltrotor achieves transition successfully with disturbances, noise and uncertainties being present.

A novel VTOL Quad Tiltrotor UAV mathematical model is developed with a special tilting rotor mechanism, which combines both aircraft and helicopter flight modes with the transition taking place in between phases using robust H-infinity controller for attitude, altitude and trajectory regulation in the presence of uncertainty.