Multirotor Drones Research Articles

Military Based Fixed Wing Scouting of an Unmanned Aerial Vehicle

Abstract: Unmanned Aerial Vehicles (UAV) is in constant development since First World War. As the technology became complex and compact, the UAVs are also being utilized for commercial purposes. The main purpose of a UAV was for scouting, to perform certain operations in remote areas with minimal human interference and to eliminate the risk of human pilots. UAVs are classified based on their weight, range of operation, type of model, and fuel consumption. At present, multi-rotor drones with high mobility are widely used for commercial sectors especially, aerial photography, sports, surveys, etc. Most of the drones are electric-based which provide a maximum of 30 minutes flight time that is sufficient for small scale applications. There are combustible fuel based drones in development, but they are very expensive, also multi-rotor drones are inefficient and provide less output per input.

Ground Crash Area Estimation of Quadrotor Aircraft Under Propulsion Failure

Small unmanned aircraft systems or drones are expected to be used for different applications, such as parcel delivery, inspection, and aerial photography, in urban areas. However, drones usually use an electric system to power up the propulsion, communications, navigation, and flight control system, which means that it is not as reliable as the manned aircraft system and may result in failure during operation and then crash to the ground. At present, there is almost no extensive publication about the high-fidelity modeling used by drones to calculate the crash trajectory and point of crash. The experimental data for modeling and simulation verification of multirotor aircraft are limited. So far, crash trajectory prediction has been limited to point mass or ballistic methods, and these methods are usually only suitable for complete power failure and without any control system. This study intends to investigate the effects of different multirotor drones’ failure modes on its crash trajectory and crash area compared to the ballistic model by using ADAMS and MATLAB cosimulation methods. Conclusions from the study show the crash trajectory, flight distance, and impact speed of the drones under four failure modes, which are quite different from the ballistic trajectory. The findings can potentially contribute to better risk assessment of the multirotor drones for the urban environment operation.

Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems.

Today, there is a conspicuous upward trend for the development of unmanned aerial vehicles (UAVs), especially in the field of multirotor drones. Their advantages over fixed-wing aircrafts are that they can hover, which allows their usage in a wide range of remote surveillance applications: industrial, strategic, governmental, public and homeland security. Moreover, because the component market for this type of vehicles is in continuous growth, new concepts have emerged to improve the stability and reliability of the multicopters, but efficient solutions with reduced costs are still expected. This work is focused on hexacopter UAV tests carried out on an original platform both within laboratory and on unrestricted open areas during the start-stop manoeuvres of the motors to verify the operational parameters, hover flight, the drone stability and reliability, as well as the aerodynamics and robustness at different wind speeds. The flight parameters extracted from the sensor systems' comprising accelerometers, gyroscopes, magnetometers, barometers, GPS antenna and EO/IR cameras were analysed, and adjustments were performed accordingly, when needed. An FEM simulation approach allowed an additional decision support platform that expanded the experiments in the virtual environment. Finally, practical conclusions were drawn to enhance the hexacopter UAV stability, reliability and manoeuvrability.

Integrated Evaluation of the Aeroacoustics and Psychoacoustics of a Single Propeller.

Aeroacoustic noise in multiple rotor drones has been increasingly recognized as a crucial issue, while noise reduction is normally associated with a trade-off between aerodynamic performance and sound suppression as well as sound quality improvement. Here, we propose an integrated methodology to evaluate both aeroacoustics and psychoacoustics of a single propeller. For a loop-type propeller, an experimental investigation was conducted in association with its aerodynamic and acoustic characteristics via a hover stand test in an anechoic chamber; the psychoacoustic performance was then examined with psychoacoustic annoyance models to evaluate five psychoacoustic metrics comprising loudness, fluctuation strength, roughness, sharpness, and tonality. A comparison of the figure of merit (FM), the overall sound pressure level (OASPL) and psychoacoustic metrics was undertaken among a two-blade propeller, a four-blade propeller, the loop-type propeller, a wide chord loop-type propeller, and a DJI Phantom III propeller, indicating that the loop-type propeller enables a remarkable reduction in OASPL and a noticeable improvement in sound quality while achieving comparable aerodynamic performance. Furthermore, the psychoacoustic analysis demonstrates that the loop-type propeller can improve the psychological response to various noises in terms of the higher-level broadband and lower-level tonal noise components. It is thus verified that the integrated evaluation methodology of aeroacoustics and psychoacoustics can be a useful tool in the design of low-noise propellers in association with multirotor drones.

An investigation on the structural vibrations of multi-rotor passenger drones

In order to solve the structural damage problem of the first generation of large multi-rotor manned drones, the present work has designed to study the structural vibration problems of multi-rotor drones. On a small multi-rotor drone, the laser vibration meter verified the reliability of acceleration sensor measurement of vibration and found that circular shape carbon fiber arms have strong damping abilities, with the strongest vibration in the Z-axis direction. To improve the design of the second generation of large multi-rotor manned drones, elliptical shape carbon fiber arms were employed instead of circular arms. Experiments showed that the main vibrations of the large multi-rotor manned drone’s arm are low-frequency vibrations below 200Hz, producing mainly torsional and bending modes, and the elliptical carbon fiber arms significantly reduce vibrations in the Z-axis direction. This study provides experimental data support for multi-rotor manned drones and further presents an improvement strategy for suppressing the vibrations of the multi-rotor manned drones.

Flight Time Optimization and Modeling of a Hybrid Gasoline–Electric Multirotor Drone: An Experimental Study

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.

Effects of owl-inspired leading-edge serrations on tandem wing aeroacoustics

Leading-edge (LE) serrations on owls’ outermost remiges play a crucial role in the silent flight of owls. While the aeroacoustic characteristics of LE serrations have been widely studied using single feathers/airfoils, how they affect feather–feather (feather slots) interactions during flight remains unclear. Here, we present a numerical analysis of the effects of owl-inspired LE serrations on the aeroacoustics of tandem wing models. Large-eddy simulations and Ffowcs Williams–Hawkings analogy are combined to resolve the flow and acoustic fields around the tandem wings. The results demonstrate that serration-induced aeroacoustics are closely associated with the gap distance (D) between fore and hind wings. At a low AoA of 5°, as D increases, the LE serrations on the fore wing initially reduce the far-field sound pressures (D < 0.22c) by passively altering the laminar–turbulent transition on upper wing surfaces but turn out to increase the sound pressures remarkedly when D > 0.22c due to the flow instability induced at the hind wing LE. However, at a high AoA of 15°, the fore wing serrations enable robust sound reductions for all gap distances by mitigating the flow instabilities in the vicinity of the fore wing trailing edge and hind wing LE. Furthermore, the combination of LE serrations on fore and hind wings is verified to be capable of bringing nonlinear synergetic effects on the suppression of flow fluctuations and noise, which can inspire innovative biomimetic designs for low-noise multirotor drones and wind turbines.

Characterisation of Fixed-Wing Versus Multirotors UAVs/Drones

Drones are Unmanned Aerial Vehicles (UAVs) that do not carry a human operator, fly remotely or autonomously, and carry lethal or non-lethal payloads. Advances in fabrication, navigation, remote control capabilities, and power storage systems have made possible the development of a wide range of drones. The most popular ones are fixed-wing and multirotor drones. They have several advantages and disadvantages and can be deployed quickly to obtain very high resolution imagery/point cloud data. With sophisticated computer vision, robotics and data, and low cost digital cameras, it is possible to get centimeter-level resolution and accuracy. Advances in technology have made the increased uses of drones for various applications. The uses of UAVs/drones are increasing allowing 2D and 3D maps to be created and used for creation of 3D maps and digital elevation models (DEMs). This paper describes in details about the two broad categories of UAVs; fixed-wing and multirotor UAVs. Their salient characteristics along with advantages and disadvantages are also given. It also provides insights to the users for selection of right kind of UAV.

A real-test and simulation combined platform for developing intelligent tracking control of multirotors

PurposeThis study aims to minimize the risk of costly failures of flight tests during the path tracking control design, and a noble approach has been proposed in this study to put the whole vehicle-in-the simulation loop. Working with the real system is essential for developing intelligent and data-driven controllers for multirotor drones which needs learning the drones' nonlinear complicated dynamics. The vehicle-in-the-loop (VIL) platform developed in this paper is a safe and effective solution to deal with this problem.Design/methodology/approachTo avoid risky flight test during controller design, the multirotor is hinged to a shaft that allows the multirotor's angular motion but restricts translational motion. The test-bed includes the real system attitude dynamics and the simulation of the position dynamics to model the complete flight based on real-time reactions of the vehicle. For the authors' case study, a hexacopter angular motion provides the real-time attitude data in translational motion simulation loop. To test the set-up, a proportional-integral-derivative (PID) and a brain emotional learning-based intelligent controller (BELBIC) is implemented for tracking of circle and 8-shape flight trajectories.FindingsThe results show that the platform helps the intelligent controller to learn the system dynamics without worrying about the failure in the early stages of the design and in the real-world flight test. Although the hexacopter translational dynamics is modeled in simulation, the authors still have highly accurate attitude dynamics matching the requirement of the control loop design. The comparison of the two controllers also shows that the performance of BELBIC is better than PID in this test.Originality/valueThe research background is reviewed in the introduction section. The other sections are originally developed in this paper.

A Backstepping Approach to Nonlinear Model Predictive Horizon for Optimal Trajectory Planning

This paper presents a novel trajectory planning approach for nonlinear dynamical systems; a multi-rotor drone, built on an optimization-based framework proposed by the authors named the Nonlinear Model Predictive Horizon. In the present work, this method is integrated with a Backstepping Control technique. The goal is to remove the non-convexity of the optimization problem in order to provide real-time computation of reference trajectories for the vehicle which respects its dynamics while avoiding sensed static and dynamic obstacles in the environment. Our method is applied to two models of multi-rotor drones to demonstrate its flexibility. Several simulation and hardware flight experiments are presented to validate the proposed design and demonstrate its performance improvement over earlier work.

An Improved Yolov5 for Multi-Rotor UAV Detection

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.

A Small UAV Optimized for Efficient Long-Range and VTOL Missions: An Experimental Tandem-Wing Quadplane Drone

Most types of Unmanned Aerial Vehicle (UAV, drone) missions requiring Vertical-Take-Off-and-Landing (VTOL) capability could benefit if a drone’s effective range could be extended. Example missions include Search-And-Rescue (SAR) operations, a remote inspection of distant objects, or parcel delivery. There are numerous research works on multi-rotor drones (e.g., quadcopters), fixed-wing drones, VTOL quadplanes, or tilt-motor/tilt-wing VTOLs. We propose a unique compact VTOL UAV optimized for long hover and long-range missions with great lifting capacity and manoeuvrability: a tandem-wing quadplane with fixed motors only. To the best of our knowledge, such a drone has not yet been researched. The drone was designed, built, and tested in flight. Construction details, its advantages, and issues are discussed in this research.

A Multi-Rotor Drone Micro-Motion Parameter Estimation Method Based on CVMD and SVD

It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target.

Propeller-integrated airfoil heater system for small multirotor drones in icing environments: Anti-icing feasibility study

Atmospheric icing is one of the most common and hazardous environmental challenges for aircraft operations. So far, only large passenger aircraft, a few general aviation aircraft, and specialized rotorcraft are equipped with ice protection systems because of their additional energy consumption and weight. The emerging markets of small electric aircraft like Urban Air Mobility and Unmanned Aerial Vehicles for transportation, logistics, search and rescue, and other specialized operations will require efficient, lightweight and all-electric ice protection systems in order to allow operations in ice and snow conditions. In this work, an electrothermal ice protection system with an average heat power density of 1Wcm−2 was designed and developed for use on small multirotor drones. Commercially available propeller blades with diameters of 33cm were retrofitted with lightweight multilayer heating foils independently powered by super-capacitors. The heated propellers were tested in close-to-real icing conditions in a small-scale icing wind tunnel. Thrust, rotational speed, and power consumption of a non-heated reference propeller and a heated propeller were compared. In glaze ice conditions at air temperatures > −5°C the thrust of the non-heated propeller fluctuated between 60 and 90% with frequent cycles of ice accretion and shedding for the entire duration of the test. The heated propeller maintained a thrust of >95% in the same icing conditions. In rime ice conditions at air temperatures < −8°C the heating was not sufficient and the thrust of the non-heated and heated propellers similarly decreased over time. Heat flux calculations along the span of the propeller were made for different icing conditions and compared to the heat power density distribution of the tested ice protection system.

Monocular Visual Autonomous Landing System for Quadcopter Drones Using Software in the Loop

Autonomous landing is a capability that is essential to achieve the full potential of multirotor drones in many social and industrial applications. The implementation and testing of this capability on physical platforms is risky and resource-intensive; hence, in order to ensure both a sound design process and a safe deployment, simulations are required before implementing a physical prototype. This article presents the development of a monocular visual system, using a software-in-the-loop methodology that autonomously and efficiently lands a quadcopter drone on a predefined landing pad, thus reducing the risks of the physical testing stage. In addition to ensuring that the autonomous landing system as a whole fulfils the design requirements using a Gazebo-based simulation, our approach provides a tool for safe parameter tuning and design testing prior to physical implementation. Finally, the proposed monocular vision-only approach to landing pad tracking made it possible to effectively implement the system in an F450 quadcopter drone with the standard computational capabilities of an Odroid XU4 embedded processor.

Light-driven microdrones.

When photons interact with matter, forces and torques occur due to the transfer of linear and angular momentum, respectively. The resulting accelerations are small for macroscopic objects but become substantial for microscopic objects with small masses and moments of inertia, rendering photon recoil very attractive to propel micro- and nano-objects. However, until now, using light to control object motion in two or three dimensions in all three or six degrees of freedom has remained an unsolved challenge. Here we demonstrate light-driven microdrones (size roughly 2 μm and mass roughly 2 pg) in an aqueous environment that can be manoeuvred in two dimensions in all three independent degrees of freedom (two translational and one rotational) using two overlapping unfocused light fields of 830 and 980 nm wavelength. To actuate the microdrones independent of their orientation, we use up to four individually addressable chiral plasmonic nanoantennas acting as nanomotors that resonantly scatter the circular polarization components of the driving light into well-defined directions. The microdrones are manoeuvred by only adjusting the optical power for each motor (the power of each circular polarization component of each wavelength). The actuation concept is therefore similar to that of macroscopic multirotor drones. As a result, we demonstrate manual steering of the microdrones along complex paths. Since all degrees of freedom can be addressed independently and directly, feedback control loops may be used to counteract Brownian motion. We posit that the microdrones can find applications in transport and release of cargos, nanomanipulation, and local probing and sensing of nano and mesoscale objects.

A Robust and Accurate Landing Methodology for Drones on Moving Targets

This paper presents a framework for performing autonomous precise landing of unmanned aerial vehicles (UAVs) on dynamic targets. The main goal of this work is to design the methodology and the controlling algorithms that will allow multi-rotor drones to perform a robust and efficient landing in dynamic conditions of changing wind, dynamic obstacles, and moving targets. Unlike existing GNSS-based vertical landing solutions, the suggested framework does not rely on global positioning and uses adaptive diagonal approaching angle visual landing. The framework was designed to work on existing camera-drone platforms, without any need for additional sensors, and it was implemented using DJI’s API on Android devices. The presented concept of visual sliding landing (VSL) was tested on a wide range of commercial drones, performing hundreds of precise and robust autonomous landings on dynamic targets, including boats, cars, RC-boats, and RC-rovers.

Social Drone Sharing to Increase UAV Patrolling Autonomy in Pre- and Post-Emergency Scenarios.

Multirotor drones are becoming increasingly popular in a number of application fields, with a unique appeal to the scientific community and the general public. Applications include security, monitoring and surveillance, environmental mapping, and emergency scenario management: in all these areas, two of the main issues to address are the availability of appropriate software architectures to coordinate teams of drones and solutions to cope with the short-term battery life. This article proposes the novel concepts of Social Drone Sharing (SDS) and Social Charging Station (SCS), which provide the basis to address these problems. Specifically, the article focuses on teams of drones in pre- and post-event monitoring and assessment. Using multirotor drones in these situations can be difficult due to the limited flight autonomy when multiple targets need to be inspected. The idea behind the SDS concept is that citizens can volunteer to recharge a drone or replace its batteries if it lands on their property. The computation of paths to inspect multiple targets will then take into account the availability of SCSs to find solutions compatible with the required inspection and flight times. The main contribution of this article is the development of a cloud-based software architecture for SDS mission management, which includes a multi-drone path-optimization algorithm taking the SDS and SCS concepts into account. Experiments in simulation and a lab environment are discussed, paving the path to a larger trial in a real scenario.

Investigation of Rotor Efficiency with Varying Rotor Pitch Angle for a Coaxial Drone

Coaxial rotor systems are appealing for multirotor drones, as they increase thrust without increasing the vehicle’s footprint. However, the thrust of a coaxial rotor system is reduced compared to having the rotors in line. It is of interest to increase the efficiency of coaxial systems, both to extend mission time and to enable new mission capabilities. While some parameters of a coaxial system have been explored, such as the rotor-to-rotor distance, the influence of rotor pitch is less understood. This work investigates how adjusting the pitch of the lower rotor relative to that of the upper one impacts the overall efficiency of the system. A methodology based on blade element momentum theory is extended to coaxial rotor systems, and in addition blade-resolved simulations using computational fluid dynamics are performed. A coaxial rotor system for a medium-sized drone with a rotor diameter of 71.12 cm is used for the study. Experiments are performed using a thrust stand to validate the methods. The results show that there exists a peak in total rotor efficiency (thrust-to-power ratio), and that the efficiency can be increased by 2% to 5% by increasing the pitch of the lower rotor. The work contributes to furthering our understanding of coaxial rotor systems, and the results can potentially lead to more efficient drones with increased mission time.

Towards monocular vision-based autonomous flight through deep reinforcement learning

This paper proposes an obstacle avoidance strategy for small multi-rotor drones with a monocular camera using deep reinforcement learning. The proposed method is composed of two steps: depth estimation and navigation decision making. For the depth estimation step, a pre-trained depth estimation algorithm based on the convolutional neural network is used. On the navigation decision making step, a dueling double deep Q-network is employed with a well-designed reward function. The network is trained using the robot operating system and Gazebo simulation environment. To validate the performance and robustness of the proposed approach, simulations and real experiments have been carried out using a Parrot Bebop2 drone in various complex indoor environments. We demonstrate that the proposed algorithm successfully travels along the narrow corridors with the texture free walls, people, and boxes.