In this paper, the characterization of 3D printed materials that are considered in the design of multirotor unmanned aerial vehicles (UAVs) for specialised purposes was carried out. The multirotor UAV system is briefly described, primarily from the aspect of system dynamics, considering that the airframe parts connect the UAV components, including the propulsion configuration, into a functional assembly. Three additive manufacturing (AM) technologies were discussed, and a brief overview was given of selective laser sintering (SLS), fused deposition modeling (FDM), and continuous fiber fabrication (CFF). Using hardware and related software, 12 series of specimens were produced which were experimentally tested utilizing a quasi-static uniaxial tensile test. The results of the experimental tests are given graphically with displacement-force characteristics. In this work, the focus is on CFF technology and the testing of materials that will be used in the production of mechanically loaded airframe parts of multirotor UAVs. Furthermore, an overview was given in such a way that the specimens were grouped, and the mean values of the maximum stress were presented, so that the tested materials could be more easily compared with conventional materials, such as aluminum alloys.

In the past few decades, the use of agricultural waste has been increasing due to the increasing demand for sustainable materials. This paper aims to review the significance of agricultural waste as a type of wealth and its potential as an alternative material in the composite field. It was found that many agricultural wastes had been utilized in various production of ecological products. The review discusses the physical, mechanical, and chemical properties of the natural fibers made from agricultural waste and the use of Pineapple Leaf Fiber Reinforced Composite to manufacture UAVs (Unmanned Aerial Vehicles). The extracted waste fibers’ physical, mechanical, and chemical properties show that these agricultural wastes can produce better advanced composite materials suitable for structural applications. The use of pineapple leaf fiber in Unmanned Aerial Vehicles manufacturing has the potential to revolutionize this industry and contribute to a more sustainable future.

Unmanned aerial vehicle (UAV) usage is increasing drastically worldwide as UAVs are used in various industries for many applications, such as inspection, logistics, agriculture, and many more. This is because performing a task using UAV makes the job more efficient and reduces the workload needed. However, for a UAV to be operated manually or autonomously, the UAV must be equipped with proper safety features. An anti-collision system is one of the most crucial and fundamental safety features that UAVs must be equipped with. The anti-collision system allows the UAV to maintain a safe distance from any obstacles. The anti-collision technologies are of crucial relevance to assure the survival and safety of UAVs. Anti-collision of UAVs can be varied in the aspect of the use of sensors and the system’s working principle. This article provides a comprehensive overview of anti-collision technologies for UAVs. It also presents drone safety laws and regulations that prevent a collision at the policy level. The process of anti-collision technologies is studied from three aspects: Obstacle detection, collision prediction, and collision avoidance. A detailed overview and comparison of the methods of each element and an analysis of their advantages and disadvantages have been provided. In addition, the future trends of UAV anti-collision technologies from the viewpoint of fast obstacle detection and wireless networking are presented.

On-stream inspections are the most appropriate method for routine inspections during plant operation without going through production downtime. Ultrasonic inspection, one of the on-stream inspection methods, faces challenges when performed at high temperatures exceeding the recommended 52°C. This study aims to determine the ultrasonic velocity and attenuation with the known material grade, thickness, and temperatures by comparing theory calculation versus experiment with temperatures ranging between 30°C to 250°C on low carbon steel covering most petrochemical equipment material and working conditions. The theoretical analysis was to obtain Young's modulus, Poisson's ratio, and longitudinal velocity at different temperatures. The experiments validated the predicted results of ultrasonic change due to temperature increase and showed a maximum error of 3%. The experimental data of velocity and decibel change from the temperature range provide a reference for the future when dealing with unknown materials information on site that require a quick corrosion status determination.

An approach to the implementation of neural network for real-time cryptographic data protection with symmetric keys oriented on embedded systems is presented. This approach is valuable especially for onboard communication systems in unmanned aerial vehicles (UAV) because of suitability to hardware implementation. In this study we evaluate the possibility to build such a system in hardware implementation at FPGA. The proposed integrated neural network approach for real-time cryptographic data protection was based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern hardware components with programmable structure. The development and implementation of the on-board neural network system for cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; hardware and software modification and making them suitable for data encryption and decryption. The tabular-algorithmic method for calculation of the scalar product has been improved that makes it possible to perform fast calculation of the scalar product of fixed-point and floating-point input data by bringing them to the largest common order of weights and forming tables of macro-partial products. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

The neural network technology for real-time cryptographic data protection with symmetric keys (masking codes, neural network architecture and weights matrix) for unmanned aerial vehicles (UAV) onboard communication systems has been developed. It provides hardware and software implementation with high technical and operational characteristics. The development of neural network technology for real-time cryptographic data protection was performed using an integrated approach based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern element base with the possibility of programming for the structure, and computer-aided design of hardware and software tools. The development and implementation of the on-board system of neural network cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; specialization and adaptation of hardware and software to data encryption and decryption. The tabular-algorithmic method of calculating the scalar product has been improved, it provides fast calculation of the scalar product of input data for both fixed and floating-point by bringing to the largest common order of weights and forming tables of macro-partial products for them. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

Petrochemical plants use on-stream inspection more often to detect and monitor the corrosion on the equipment and piping system. Compared to ultrasonic thickness gauging and pulse-echo A-scan, phased array corrosion mapping has better coverability and can scan a large area to detect general and localized corrosion. This paper's objective is to obtain documentary evidence on the accuracy of corrosion detection from 30°C to 250°C on A36 low-carbon steel by carrying out simulation experiments every 10°C steps. A minimum of three sets of phased array corrosion mapping data in each temperature were collected to study and evaluate the detectability. The data evidence could enhance the confidence level of the plant's end users to use the phased array mapping in the future during an inspection. The experiments were found to be insufficiently thorough despite addressing the initial concerns, leaving more area for discussion in further studies, such as expanding the investigation to thicker carbon steel, stainless steel, and wedge materials.

The aerospace industry demands high-performance materials that can withstand extreme conditions and maintain efficiency in a variety of applications. Bio-inspired polymer coatings have emerged as a promising approach to meet these demands, by drawing inspiration from natural systems to develop new coatings that exhibit enhanced properties, such as self-cleaning, anti-icing, thermal management, and corrosion resistance. In this review, recent developments in the field of bio-inspired polymer coatings for aerospace applications are presented, covering a range of coatings and their respective properties. We discuss the recent developments and applications of bio-inspired coatings, with a focus on their advantages and challenges in aerospace applications. We also highlight the potential for future research and development in this field, including the integration of advanced technologies such as nanotechnology and additive manufacturing. The review aims to provide insights into the current state-of-the-art of bio-inspired polymer coatings for aerospace applications and to inspire further research in this rapidly growing field.

Mobile robots can perform tasks on the move, including exploring terrain, discovering landmark features or moving a load from one place to another. This group of robots is characterized by a certain level of intelligence allowing making decisions and responding to stimuli received from the environment. As part of Industry 5.0, such mobile robots and humans are expected to co-exist and work together in a shared environment to make human work less tiring, quicker, and safer. This can only be realized when clean, dense, and economical energy sources are available. The aim of the study is to analyze the state of the art and to identify the most important directions for future developments in robotic power systems based mainly on batteries. The efficiency and performance of the battery depends on the design using different materials. Work environments and performance requirements are considered in this systematic review to classify solutions that help developers choose the best suited power system for specific application. Indirectly, the aim of the work is to generate discussion within the scientific and engineering community. A narrative review of publications from six major bibliographic databases according to preset inclusion criteria was combined with a critical analysis of current and future technologies. The main findings of the review allow answering question what is the role of modern power source technologies, artificial intelligence and ground-breaking research work in global policies related to energy saving, green policies, and sustainable development. The main opportunities and threats are discussed, and a brief feasibility analysis is carried out. Novelty of the article relates not only to the analysis of technologies, but also to approaches and their use under conditions of limited resource availability, when resource usage must be minimized. The article provides an overview of batteries, their specifications, classifications, and their advantages and disadvantages. In addition, we propose: (1) an algorithm for selecting main energy source for robot application, and (2) an algorithm for selecting electrical system power supply. Current mobile robot batteries are, in most cases, their biggest limitation. Progress in batteries development is too slow to catch up with the demand for robot autonomy and range requirements, limiting the development of mobile robots. Further intensive research and implementation work is needed to avoid years of delay in this area.

Over the past few years, there has been an increasing fascination with electric unmanned aerial vehicles (UAVs) because of their capacity to undertake demanding and perilous missions, while also delivering advantages in terms of flexibility, safety, and expenses. These UAVs are revolutionizing various public services, encompassing real-time surveillance, search and rescue operations, wildlife assessments, delivery services, wireless connectivity, and precise farming. To enhance their efficiency and duration, UAVs typically employ a hybrid power system. This system integrates diverse energy sources, such as fuel cells, batteries, solar cells, and supercapacitors. The selection of an appropriate hybrid power arrangement and the implementation of an effective energy management system are crucial for the successful functioning of advanced UAVs. This article specifically concentrates on UAV platforms powered by batteries, incorporating innovative technologies like in-flight recharging via laser beams and tethering. It provides an all-encompassing and evaluative examination of the current cutting-edge power supply configurations, with the objective of identifying deficiencies, presenting perspectives, and offering recommendations for future consideration in this domain.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

Natural fibers have been widely used as a reinforcing material for the construction of drones. The main goal of this review is to identify a biodegradable natural fiber material that can be built for Unmanned Aerial Vehicles (UAVs). The review shows that natural fibers such as kenaf and PALF can be used as an alternative composite material, particularly for multifunctional applications where their smaller weight and less expensive characteristics make them incredibly realistic. This review also discusses the mechanical properties, advantages and disadvantages, and fabrication of the composites.

In order to address the issues of weight, cost and sustainability issues associated with the application of glass fibre in structural composites, plant fibres are proposed as an alternative to reduce and replace the usage of glass fibres. Despite this, there is still a gap in comprehensive research on plant fibre composites, especially with regard to their durability in real structural applications. This research investigates the fatigue and impact properties of pultruded hybrid polyester composites reinforced with glass fibre and kenaf, tailored for structural applications. Utilising kenaf fibres in mat form, unidirectional glass fibre direct roving yarns and unsaturated polyester resin as key components, pultruded kenaf/glass hybrid profiles were meticulously fabricated. Comparative cost and weight analyses were conducted between pultruded hybrid and non-hybrid profiles. The study showed that the pultruded kenaf/glass alternative laminates exhibit commendable fatigue and impact resistance, making them suitable for use in moderately loaded structural applications. Moreover, the use of kenaf as a hybrid alternative shows significant cost and weight reductions. This research provides a basis for the development of sustainable and efficient structural materials and highlights the significant role of material design in shaping the future of engineering applications.

This study examines the impact of alcohol concentration on the precision of unmanned aerial vehicle (UAV) operations. While the impact of alcohol impairment on various activities is well documented, its specific influence on drone piloting accuracy remains less studied. To address this, we carried out empirical research using an original methodology and a real-world environment. The study involved a group of 35 participants performing a series of flights using a selected drone (DJI Mavic2 Pro) on a bespoke obstacle route. Between each flight series, participants consumed a measured amount of alcohol, designed to incrementally increase their blood alcohol concentration monitored via breath tests. The precision of the drone pilot was evaluated by comparing various parameters from successive test flights, such as time, distance, average speed, and incident frequency, against a baseline trial executed with zero alcohol concentration. Our results indicate a noticeable decline in UAV piloting precision corresponding with increasing alcohol concentration. Parameters such as flight time, distance, speed, and incident frequency all demonstrated this trend. We conclude that alcohol consumption detrimentally affects the accuracy of drone operations. These findings underscore the need for clear policies and guidelines regarding the operation of UAVs under the influence of alcohol.