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The primary purpose of this study is to analyse the performance of multirotor unmanned aircraft system platforms for passenger transport and compare them with an ordinary helicopter solution. This study aims to define a standard procedure for power budget analysis of unconventional vehicles recently proposed in the aerospace industry, providing guidelines on rotor sizing in terms of required power and the total number of rotors. The ultimate purpose of the proposed work is to describe a methodology for power estimation with regard to emerging electric vertical takeoff and landing (EVTOL) vehicles.

In the context of urban mobility, short-range passenger transport between critical hubs in cities is taken into account and innovative aircraft and traditional helicopters are compared according to a common mission profile. The power budget equations used in the helicopter literature are revisited to consider different multirotor configurations (up to 20 rotors) and evaluate the feasibility of innovative aerospace vehicle design.

The paper includes insights into the maximum number of rotors that ensure a significative, relative power reduction compared to helicopter platforms (the power-to-cruise over power-to-hover ratio appears to be improved). Based on this preliminary analysis, the results suggest the benefit of reducing the installed rotors to avoid excessive power loss in forward flight.

The proposed study provides guidelines for further design considerations and the future development of EVTOL multirotor aircraft.

This paper fulfils the identified need for a systematic approach on performance analysis for innovative vehicles involved in commercial applications.

This paper aims to reveal the authors’ conceptual and experimental work on an innovative avionics paradigm for small unmanned aerial vehicles (UAVs).

This novel approach stipulates that, rather than being centralized at the autopilot, control of avionics devices is instead distributed among controllers – spread over the airframe span, in response to avionics devices’ natural location requirements. The latter controllers are herein referred to as edge controllers by the first author.

The edge controller manifests increased efficiency in a number of functions, some of which are unburdened from the autopilot. The edge controller establishes a new paradigm of structure and design of small UAVs avionics such that any functionality related to the periphery of the airframe is implemented in the controller.

The research encompasses a workbench prototype testing on a breadboard, as the presented idea is a novel concept. Further, another test has been conducted with four controllers mounted on a quadcopter; results from the vertical attitude sustenance are disclosed herein.

The motivation behind developing this paradigm was the need to position certain avionics devices at different locations on the airframe. Due to their inherent functional requirements, most of these devices have hitherto been placed at the periphery of the aircraft construction.

The current paper describes the novel avionics paradigm, compares it to the standard approach and further reveals two experimental setups with testing results.

Dual-thrust hybrid unmanned aerial vehicle (UAV) technology offers a highly robust and efficient system that incorporates the take-off and landing capabilities of rotary-wing aircraft with the endurance capacities of fixed-wing aircraft. The purpose of this study is to model and control a hybrid UAV in three distinct flight modes: rotary-wing, fixed-wing and over-actuated model.

Model predictive control (MPC) along with linear models are applied to design controllers for the rotary-wing or vertical take-off and transition to the fixed-wing flight. The MPC algorithm is implemented with two approaches, first in its usual form and then in a new form with the help of tracking error variables as state variables.

Because the tracking error variables are more compatible with the cost function used in MPC, the results improve significantly. This is especially important for a safe and stable transition from rotary-wing to fixed-wing flight, which should be done quickly. The authors also propose a control allocation strategy with MPC algorithm to exploit the thrust and control inputs of both rotary-wing and fixed-wing systems for the transition phase. As the control system is over-actuated, the proposed algorithm distributes the control signal among the actuators better than the MPC alone. The numerical results show that the flight trajectory is also improved.

The research background is reviewed in the introduction section. The other sections are originally developed in this paper to the best of the authors’ knowledge.

This study aims to optimize autonomous performance (i.e. both longitudinal and lateral) and endurance of the quadrotor type aerial vehicle simultaneously depending on the autopilot gain coefficients and battery weight.

Quadrotor design processes are critical to performance. Unmanned aerial vehicle durability is an important performance parameter. One of the factors affecting durability is the battery. Battery weight, energy capacity and discharge rate are important design parameters of the battery. In this study, proper autopilot gain coefficients and battery weight are obtained by using a stochastic optimization method named as simultaneous perturbation stochastic approximation (SPSA). Because there is no direct correlation between battery weight and battery energy density, artificial neural network (ANN) is benefited to obtain battery energy density corresponding to resulted battery weight found from SPSA algorithm. By using the SPSA algorithm optimum performance index is obtained, then obtained data is used for longitudinal and lateral autonomous flight simulations.

With SPSA, the best proportional integrator and derivative (PID) coefficients and battery weight, energy efficiency and endurance were obtained in case of morphing.

It takes a long time to find the most suitable battery values depending on quadrotor endurance. However, this situation can be overcome with the proposed SPSA.

It is very useful to determine quadrotor endurance, PID coefficients and morphing rate using the optimization method.

Determining quadrotor endurance, PID coefficients and morphing rate using the optimization method provides advantages in terms of time, cost and practicality.

The proposed method improves quadrotor endurance. In addition, with the SPSA optimization method and ANN, the parameters required for endurance will be obtained faster and more securely. In addition, the energy density according to the battery weight also contributes to the clean environment and energy efficiency.

The current manuscript aims to propose a novel multirotor design.

This paper presents a novel 16-rotor multicopter design named Emerald. The novel design innovations and benefits are disclosed. Comparison to existing 16-rotor designs is carried out. Implementation areas where the novel idea shall yield benefit are discussed. A prototype of the presented design is described.

The herein proposed 16-rotor design has a number of benefits over existing 16-rotor multicopters. The paper elaborates on those advantages.

The research was limited to prototype testing, as the presented design is a novel concept.

The motivation to research and develop this novel design is implementing the vehicle for stereoscopic photography and reconnaissance. The design is also applicable to carrying payloads while flying indoors.

This paper presents a reflection on the effectiveness of using drones in the technical inspection of building facades, as a practical alternative to more expensive and heavier conventional means. In order to verify this assumption qualitatively, a number of field tests are presented and discussed.

The first stage consisted of a literature review. The analysis initially investigated drone technology. Then, an insight into some general applications in Civil Engineering was compiled, focusing in particular on the Construction Industry (CI), as well as the scope of building envelope inspections. In the second stage, field tests with drones were performed and the resulting data was appraised in order to get facade pathologies diagnosis as accurate as possible.

Through this study, the adaptability and versatility of using drones were checked. Thus, it has been confirmed that drone technology is an effective and promising alternative methodology to support technical inspection and diagnosis of building envelope pathologies. In fact, drones seem to be a worthwhile tool for supporting certain Civil Engineering activities–particularly when displaying a multirotor configuration, loading high-definition (HD) cameras, for both stills photography and video.

As the main goal of this approach was simply a qualitative assessment of evidence on the advantages of using drones compared with conventional means, other kinds of comparative analysis were not discussed in depth. However, this and other potential restrictive issues may represent an opportunity to push more developments. A deeper knowledge of data handling and clear procedures for the use of drones could be very valuable for the CI and for building envelope inspections.

In investigating this subject, this research can offer valuable input to enable a greater awareness of the use of drones in the technical inspection of buildings, particularly in hard-to-reach locations, to the detriment of conventional means. In these circumstances, this methodology can bring great benefits, particularly from an environmental impact point of view, since the resources used are much lower. In addition, a very reliable diagnosis can be achieved more quickly, and at a much lower cost.

The introduction of a standard for drone inspection practice is still at an early stage, with initial steps being taken in an apparently well-focused effort to achieve consolidation. Research studies of this kind may have an important role to play in drawing the stakeholders' attention to the benefits of using this technique, particularly for those working in the building usage phase.

The purpose of this paper is to optimize the simultaneous flight performance of a hexarotor unmanned aerial vehicle (UAV) by using simultaneous perturbation stochastic approximation (i.e. SPSA), deep neural network and proportional integral derivative (i.e. PID) according to varying arm length (i.e. morphing).

In this paper, proper PID gain coefficients and morphing ratio were obtained using the stochastic optimization method, also known as SPSA to maximize flight efficiency. Because it is difficult to establish an analytical connection between the morphing ratio and hexarotor moments of inertia, the deep neural network was used to obtain the moments of inertia according to the morphing ratio. By using SPSA and deep neural network, the best performance indexes were obtained and both longitudinal and lateral flight simulations were performed with the obtained data.

With SPSA, the best PID coefficients and morphing ratio are obtained for both longitudinal and lateral flight. Because the hexarotor solid body model changes according to the morphing ratio, the moment of inertia values used in the simulations also change. According to the morphing ratio, the moment of inertia values was obtained with the deep neural network over a created data set.

It takes a long time to obtain the morphing ratio suitable for the hexarotor model and the PID gain coefficients suitable for this morphing ratio. However, this situation can be overcome with the proposed SPSA. In addition, it takes a long time to obtain the appropriate moments of inertia according to the morphing ratio. However, in this case, it was overcome using the deep neural network.

Determining the morphing ratio and PID gain coefficients using the optimization method, as well as determining the moments of inertia using the deep neural network, is very useful as it can increase the efficiency of hexarotor flight and flight efficiently with different arm lengths. With the proposed method, the hexarotor design performance criteria (i.e. rise time, settling time and overshoot) values were significantly improved compared to similar studies.

Determining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability.

The hexarotor flight efficiency is improved with the proposed SPSA and deep neural network approaches. In addition, the desired flight parameters can be obtained more quickly and reliably with the proposed approaches. The design performance criteria were also improved, enabling the hexarotor UAV to follow the given trajectory in the best way and providing convenience for end users. SPSA was preferred because it converged faster than other methods. While other methods perform 2n operations per iteration, SPSA only performs two operations. To obtain the moment of inertia, many physical parameter values of the UAV are required in the existing methods. In the proposed method, by creating a date set, only arm length and moment of inertia were estimated without the need to obtain physical parameters with the deep neural network structure.

High rate of accidents continue to plague the construction industry. The advancements in safety technologies can ameliorate construction health and safety (H&S). This paper aims to explore the use of emerging technologies as an effective solution for improving safety in construction projects.

Following a detailed literature review, a questionnaire survey was developed encompassing ten technologies for safety management and ten safety enablers using technologies in construction. A total of 133 responses were gathered from Malaysian construction practitioners. The collected quantitative data were subjected to descriptive and inferential statistical analyses to determine the meaningful relationships between the variables.

Findings revealed that the most effective emerging technologies for safety management are: building information modelling (BIM), wearable safety technologies and robotics and automation (R&A). The leading safety enablers are related to improve hazard identification, reinforce safety planning, enhance safety inspection, enhance safety monitoring and supervision and raise safety awareness.

Safety is immensely essential in transforming the construction industry into a robustly developed industry with high safety and quality standards. The adoption of safety technologies in construction projects can drive the industry towards the path of Construction 4.0.

The construction industry has historically been slow to adopt new technology. This study contributes to advancing the body of knowledge in the area of incorporating emerging technologies to further construction safety science and management in the context of the developing world. By taking cognisance of the pertinent emerging technologies for safety management and the safety enablers involved, construction safety can be enhanced using integrated technological solutions.

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.

This chapter examines the proposition that drone technologies are a force for good and discusses a number of applications that have become prominent within this category. It investigates how drones are being used within a growing number of sectors, including leisure, research, health, journalism, and sports, and analysing the development of such pursuits and their implications. It focusses on what resides behind the desire to re-characterise drones as objects of desire – and products more generally – and as vehicles for positive social change. Central to this chapter is the claim that drones are vehicles for asserting agency and competence over technology, but that there is also a trend towards the erosion of skills required to operate such devices, which undermines their overall evaluation has sources of empowerment.