Characterizing thermodynamic observations from unshielded multirotor drone sensors

Increased flight time of multirotor drones is a key enabler for further adoption and industrial use of drones. A model for analyzing the performance of a fuel cell hybrid system for a multirotor drone is presented and applied for a case with an X8 multirotor drone with a maximum take-off mass of 25 kg. Endurance is the main performance parameter, and the model can be used to quantify the relative performance between different power sources. The model aims to determine if a specific hybrid fuel cell system is a viable option for a given multirotor drone and if it will provide better endurance than when powered by batteries. The model can also be used in system optimization and sensitivity analysis. In a case study, a fuel cell hybrid system with a 7.2 L cylinder with hydrogen at 300 bar is found to increase the flight time by 43 minutes (+76%) from the currently used LiPo-batteries. A plot identifies the energy system mass threshold for when the fuel cell hybrid system gives better endurance than batteries to be 7.3 kg. Based on current technology status, the cost of a fuel cell hybrid system is about 12 times that of LiPo-batteries.

Creating the correct maintenance of the strings that form the electric generators, from a photovoltaic park, it requires scanning of the equipment with thermal vision cameras, in the visible and infrared field, at an optimum angle determined precisely for each location depending on the geographic position and the architecture of the power station. In general, this angle is difficult to maintain from the ground so it is necessary to mount the cameras on a support, or most efficiently on a multi-rotor drone that is very stable and easy to handle. Maintenance using multi-rotor drones and heat sensitive cameras is no longer a novelty and has multiple advantages: low costs, minimization of work accidents, and the data acquired can be analyzed in real time or can be stored for later analysis. But this process also has disadvantages: limited flight autonomy of drones, generated by the hard development of battery technology and a small number of specialized personnel in this field. To minimize these disadvantages, we have developed an automatic recharging system of batteries, without disconnecting them, which allows the automation of the scanning process and offers the possibility of taking control of the drones and from a distance.

The use of multirotor drones for industrial applications is accelerating, and fuel cell based propulsion systems are highlighted as a promising approach to improve endurance – one of the current main limitations. Due to multirotor drones’ unique requirements, careful system design is needed to maximize the performance advantage. In this work a sensitivity analysis that quantifies the impact of central system parameters for an X8 multirotor drone with a 2 kW fuel cell hybrid system is presented and discussed. Thrust stand measurements identified a 20–30% efficiency loss from the coaxial configuration, and a ‘single’ configuration can reduce power consumption by 700 W at 25 kg take-off mass. Thus, a smaller fuel cell system can be used, giving an additional 1 kg mass saving and 75–140 W power reduction. Peak endurance is found at a 0.67 energy system weight fraction, and if batteries are improved from 180 Wh/kg to 350 Wh/kg, the energy system mass threshold from where fuel cells are superior rises from 7.4 kg to 8.5 kg. At 700 bar, a 3 L hydrogen cylinder can replace a 6 L at 300 bar, provide a 72-min endurance, and is the preferred option to reach minimum system volume. This work provides guidance in early conceptual stages and insights on how fuel cell based powerplants for multirotors can be improved and optimized to increase their value proposition. Further research can expand the work to cover other system variations and do experimental testing of system performance.

ABSTRACTMultirotor drones are considered a new and innovative technology. Therefore, many fields are showing increasing interest in utilizing multirotor drones, such as mapping in mining and surveillance in transportation. The construction industry has been a slow adopter of novel technologies. However, multirotor drones have potential to facilitate construction in many aspects. There is, therefore, a need to extensively research their applications and analyze their roles in construction engineering and management. This paper aims to comprehensively investigate the current applications of multirotor drones, analyze their benefits and explore their potential in the future of the construction industry. Several main aspects are reviewed and discussed, namely land surveying, logistics, on-site construction, maintenance and demolition. The results reveal that the main contributions are work safety, cost-effectiveness and carbon emission reduction, while there are possible adverse impacts on the basis of current limitations of multirotor drones. However, it can be predicted that the usefulness of drones will continue to increase in the future of the construction industry. Thus, this study will benefit construction managers in raising awareness of the use of these emerging technologies and researchers in further exploring applications of multirotor drones in construction projects.

Multi-rotor drones have a wide range of applications in practical scenarios; however, the use of multi-rotor drones for illegal acts is also on the rise, in order to improve the recognition accuracy of multi-rotor drones. A new multi-rotor drone detection algorithm is proposed. Firstly, the Yolov5 backbone is replaced with Efficientlite, thus reducing the number of parameters in the model. Secondly, adaptively spatial feature fusion is injected into the head of the baseline model to facilitate the fusion of feature maps with different spatial resolutions, in order to balance the accuracy loss caused by the lightweight of the model backbone. Finally, a constraint of angle is introduced into the original regression loss function to avoid the mismatch between the prediction frame and the real frame orientation during the training process in order to improve the speed of network convergence. Experiments show that the improved Yolov5s exhibits better detection performance, which provides a superior method for detecting multi-rotor UAVs in real-world scenarios.

The multirotor drones have been used extensively in modern farming. This device can help to tackle many tasks that is limited to human such as planting, watering, and so on. Hence, it can save farmer’s time and energy. Drones mostly use battery or supercapacitor as their power supply. It means that they cannot fly forever without charged. Battery recharging takes time. Hence, some researches have already conducted to make the battery charging easy to do the drone can continue to do the tasks without the need to stop the operation. In this research, we propose a concept model for battery recharge without stopping the drone from operation. It converts sounds from multirotor drone’s motor into current by using piezoelectric sensor. Based on calculation and analysis on frequency spectrum, ideal fundamental frequency is 120 Hz that yields length of piezoelectric sensor’s resonator 11 cm.

The electric propulsion system serves as the power source for multi-rotor drones, helping them complete various maneuvering actions. The reliability of this system directly affects whether the drone can successfully complete its mission. The multi-rotor drone propulsion system is a k-out-of-n system with functional dependence (FDEP). With the insufficient basis for selecting k-values, the problem of incalculable reliability caused by computational space explosion due to voting gates, and the uncertain impact of functional dependence on system reliability, we propose a reliability evaluation method based on controllability theory and BN (Bayesian network) reconstruction. The drone is dynamically modeled, and a control model is built, and k-values are selected through different failure combination controllability evaluations. We model the system with BN, use functional dependent components as BN node inputs, and reconstruct BN via an adder model to solve the problem of exponential growth in the conditional probability table. This paper analyzes system reliability, safety, and the impact of FDEP on the system, and conducts component importance analysis. The result provides important reference for the reliability, safety assessment, and dynamic maintenance processes of multi-rotor drone.

Crop depredation by blackbirds (Icteridae) results in substantial economic losses to the United States sunflower industry, and a solution to effectively reduce damage remains elusive. We evaluated the utility of uncrewed aircraft systems (UAS), or drones, as hazing tools to deter foraging blackbirds from commercial sunflower ( Helianthus annuus ) fields in North Dakota, USA, between September and October 2017. We compared the efficacy of 3 drones: a fixed‐wing predator model mimicking the form of an aerial raptor, a fixed‐wing airplane of similar size, and a multirotor drone. Multirotor drones are relatively easy to fly and are a multifunctional tool for agricultural use; however, they may not be an effective avian deterrent due to a lack of similarity in appearance with natural predators. Free‐ranging blackbird flocks ( n = 58) reacted to every drone approach by initiating flight and took flight 1.6 times sooner for the fixed‐wing predator model (flight initiation distance [FID] = 90 m) and 1.8 times sooner for the fixed‐wing airplane (FID = 98 m) compared to the multirotor drone (FID = 55 m). However, the probability of a blackbird flock ( n = 53) abandoning a field was greater with smaller field and flock sizes, rather than the specific drone deployed. In an applied setting, the performance of drones as avian hazing devices will likely depend on a combination of factors including platform selection, drone trajectory, duration of use, season, landscape context, and natural history of the pest species.

Drones have evolved rapidly over the decades, but the limited flight time inhibits multirotor drones from performing long-duration tasks. Batteries that power drones are considered an inadequate power source due to their low energy density. As gasoline is an energy-dense source, combining an electric propulsion system with gasoline engines should be considered. This paper proposes a novel hybrid multirotor drone design using two gasoline engines to provide the majority of the lift force and four electric motors to stabilize the drone. These propulsion systems have been characterized and optimized to exploit their respective advantages, which reduce the total energy consumption rate and increase flight time. Simulation and experimental results show that the hybrid gas–electric multirotor drone can achieve more than three times the flight time of the fully electric drone.

This paper presents a new formulation of controlling cooperative drones using relative Jacobian. This formulation affords relative movement of the end-effector drone with respect to the base drone, while the base drone moves with respect to the world frame. The relative movement releases unnecessary constraints in the movement of the drone with respect to each other, such that the relative movement between the drones now has increased degrees-of-freedom compared to absolute movement control. The implementation is shown in the Gazebo RotorS simulation platform using two firefly drones each with six propellers. The individual control of each firefly drone is using a new formulation that is also presented in this work using a Jacobian-based controller. This type of controller is versatile because it can lead towards Jacobian-based controllers of manipulators to be applied to controlling multi-rotor drones. This makes it possible to control more than one multi-rotor drone using a single controller. Frames of reference are assigned at the centre of gravity for each multi-rotor drone, and the derivation of individual drones Jacobian and the relative Jacobian are based on these reference frames. Simulation results are shown to demonstrate the effectiveness of this approach.

This paper aims to design and fabricate a prototype of an autonomous multirotor drone that can dock into a larger fixed-wing drone for Mars exploration. The multirotor must be able to locate and dock into the fixed-wing drone with only a thermal imaging sensor and inertial measurements using on-board image processing capabilities. Engineering analysis was performed to design both the fixed-wing and multirotor drones. Two quadcopters are constructed using off-the-shelf components, including small thermal cameras, hobby drone components, Raspberry Pis, and various sensors and peripherals. Electromagnetic coils mounted on the fixed-wing drone are used to interface with neodymium magnets mounted on the top of both multirotors. An image processing algorithm has been created to locate the relatively hot electromagnetic coils compared to the background temperature for locating docking points. This design indicates a novel combination of fixed-wing and multirotor drones for planetary exploration.

A first principles understanding of the sound field produced by multirotor drones in hover is presented. Propeller diameters ranging from 8 to 12 in. are examined and with configurations comprising an isolated rotor, quadcopter, and hexacopter configuration. The drone pitch, defined as the ratio of drone diameter to rotor diameter, is the same for all multirotor configurations and is valued at 2.25. A six-degree-of-freedom load cell is used to assess the aerodynamic performance of each configuration, whereas an azimuthal array of in. microphones, placed between two and three hub-center diameters from the drone center, is used to assess the acoustic near field. The analysis is performed using standard statistical metrics such as sound pressure level and overall sound pressure level and is presented to demonstrate the relationship between the number of rotors, the drone rotor size, and its aerodynamic performance (thrust) relative to the near-field acoustics.

Improved sliding mode temperature control of hydrogen fuel cells for multirotor drones

A simulation study on oil rigs inspection via virtual reality system using drones aiming to increase the efficiency of the work done during the pandemic era is presented. The usage of drones in virtual reality environment does not only provide sufficient on-site training for the operators, also able to reduce time consumption and labor demands in carrying out inspections and eliminate errors of drone crashing and hazards. Thus, this project covers the construction of virtual simulation framework using drones via Unity 3D, an open-source game engine and multi-rotor drones are selected as the drive mechanism due to its best maneuverability and stability. A 3D model of oil rig is constructed using Unity 3D that consist of equipment such as vessels, tanks, cranes, control rooms and flares like the real-life oil rigs. A 3D model of multi-rotor drone is also developed to produce a dynamic model that can wander around the oil platform. The process of scripting is done in Visual Studio 2019 using C# programming language which is one of the Unity 3D tools, making it essential for modelling and simulation development. Virtual reality features are gained with the presence of virtual headsets, static 3D modelling of oil rigs and dynamic 3D modelling of multi-rotor drones. The use of virtual reality system using drones in oil and gas platform has been successfully integrated to obtain the simulation result.

Unmanned aerial vehicles (UAVs) or drones have been proposed as deterrent tools to mitigate pest birds’ problems. Many studies have been conducted to evaluate the efficacy of drones, mainly to protect crops, fishponds and airports. Little information can be acquired on using drones in industrial areas. In this study, two types of drones, categorized as multirotor drones and fixed-wing drones, were used to evaluate their efficacy in reducing pest birds, Asian glossy starling (Aplonis panayensis) flocks in one of the semiconductor factories in Kulim Hi-tech Park, Kedah, Malaysia during dusk. Each drone was evaluated during its five minutes of operation time and five minutes after landing. Control data were also taken to compare drone treatment days with no drone treatment days. Our result shows a significant difference between multirotor drone treatment and control treatment but not between fixed-wing drone treatment and control treatment due to different altitudes applied, ambient light intensity and size of flight path covered. We suggest implementing biomimetic design into drones and applying other conventional ground deterrents to prolong the residual effect of post-treatment.