Multirotor Drones Research Articles

Are Modern Market-Available Multi-Rotor Drones Ready to Automatically Inspect Industrial Facilities?

Industrial inspection is a well-known application area for unmanned aerial vehicles (UAVs), but are modern market-available drones fully suitable for inspections of larger-scale industrial facilities? This review summarizes the pros and cons of aerial large-scale facility inspection, distinguishing it from other inspection scenarios implemented with drones. Moreover, based on paper analysis and additionally performed experimental studies, it reveals specific issues related to modern commercial drone software and demonstrates that market-available UAVs (including DJI and Autel Robotics) more or less suffer from the same problems. The discovered issues include a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) shift, an identification of multiple images captured from the same point, limitations of custom mission generation with external tools and mission length, an incorrect flight time prediction, an unpredictable time of reaching a waypoint with a small radius, deviation from the pre-planned route line between two waypoints, a high pitch angle during acceleration/deceleration, an automatic landing cancellation in a strong wind, and flight monitoring issues related to ground station software. Finally, on the basis of the paper review, we propose solutions to these issues, which helped us overcome them during the first autonomous inspection of a 2400 megawatts thermal power plant.

The impact of Bacillus thuringiensis var. israelensis (Vectobac® WDG) larvicide sprayed with drones on the bio-control of malaria vectors in rice fields of sub-urban Kigali, Rwanda

BackgroundThe core vector control tools used to reduce malaria prevalence are currently long-lasting insecticidal nets (LLINs), and indoor residual spraying (IRS). These interventions are hindered by insecticide resistance and behavioural adaptation by malaria vectors. Thus, for effective interruption of malaria transmission, there is a need to develop novel vector control interventions and technologies to address the above challenges. Larviciding using drones was experimented as an innovative tool that could complement existing indoor interventions to control malaria.MethodsA non-randomized larviciding trial was carried out in irrigated rice fields in sub-urban Kigali, Rwanda. Potential mosquito larval habitats in study sites were mapped and subsequently sprayed using multirotor drones. Application of Bacillus thuringiensis var. israelensis (Bti) (Vectobac® WDG) was followed by entomological surveys that were performed every two weeks over a ten-month period. Sampling of mosquito larvae was done with dippers while adult mosquitoes were collected using CDC miniature light traps (CDC-LT) and pyrethrum spraying collection (PSC) methods. Malaria cases were routinely monitored through community health workers in villages surrounding the study sites.ResultsThe abundance of all-species mosquito larvae, Anopheles larvae and all-species pupae declined by 68.1%, 74.6% and 99.6%, respectively. Larval density was reduced by 93.3% for total larvae, 95.3% for the Anopheles larvae and 61.9% for pupae. The total adult mosquitoes and Anopheles gambiae sensu lato collected using CDC-Light trap declined by 60.6% and 80% respectively. Malaria incidence also declined significantly between intervention and control sites (U = 20, z = − 2.268, p = 0.023).ConclusionsThe larviciding using drone technology implemented in Rwanda demonstrated a substantial reduction in abundance and density of mosquito larvae and, concomitant decline in adult mosquito populations and malaria incidences in villages contingent to the treatment sites. The scaling up of larval source management (LSM) has to be integrated in malaria programmes in targeted areas of malaria transmission in order to enhance the gains in malaria control.

A Review on Key Technologies and Developments of Hydrogen Fuel Cell Multi-Rotor Drones

A Review on Key Technologies and Developments of Hydrogen Fuel Cell Multi-Rotor Drones

Near- and Far-Field Acoustic Characteristics and Sound Source Localization Performance of Low-Noise Propellers with Gapped Gurney Flap

With the rapid industrialization utilizing multi-rotor drones in recent years, an increase in urban flights is expected in the near future. This may potentially result in noise pollution due to the operation of drones. This study investigates the near- and far-field acoustic characteristics of low-noise propellers inspired by Gurney flaps. In addition, we examine the impact of these low-noise propellers on the sound source localization performance of drones equipped with a microphone array, which are expected to be used for rescuing people in disasters. Results from in-flight noise measurements indicate significant noise reduction mainly in frequency bands above 1 kHz in both the near- and far-field. An improvement in the success rate of sound source localization with low-noise propellers was also observed. However, the influence of the position of the microphone array with respect to the propellers is more pronounced than that of propeller shape manipulation, suggesting the importance of considering the positional relationships. Computational fluid dynamics analysis of the flow field around the propellers suggests potential mechanisms for noise reduction in the developed low-noise propellers. The results obtained in this study hold potential for contributing to the development of integrated drones aimed at reducing noise and improving sound source localization performance.

A Review on the State of the Art in Copter Drones and Flight Control Systems.

This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as the most attractive for individual and research use. This paper analyzes various multirotor drone types, their construction, typical areas of implementation, and technology used underneath their construction. Finally, it looks at current challenges and future directions in drone system development, emerging technologies, and future research topics in the area. This paper concludes by highlighting some key challenges that need to be addressed before widespread adoption of drone technologies in everyday life can occur. By summarizing an up-to-date survey on the state of the art in copter drone technology, this paper will provide valuable insights into where this field is heading in terms of progress and innovation.

Improving the flight endurance of multi-rotor drones in windy days

Most recently, the technology of unmanned vehicles/systems (UVs/USs) has experienced substantial growth. These vehicles can operate on land, in water as well as and even through the air. They have become increasingly important in various civil applications, incorporating surveillance, precise farming, imagery collection, and search and rescue operations, surpassing manned systems in many aspects. Increased mission safety and cheaper operating expenses are provided by unmanned vehicles. UAVs, often known as unmanned aerial vehicles, are one of them that are widely utilized in construction projects because of its benefits including low costs for upkeep, simple deployment, the capacity to hover, and outstanding mobility. The most significant challenge facing the application of drones is their endurance, especially in harsh and windy weather conditions where drones consume power at a faster rate. In this paper, this work explores improvements in drone endurance through lightweight material design, battery enhancements, and path planning by studying and organizing relevant literature from various authors. These advancements aim to effectively extend the flight time of drones, thereby enabling them to successfully complete missions.

Comparative analysis of fixed-pitch and variable-pitch control systems for multirotor drones: Acoustic characteristics and rotor phase control

In this study, the aerodynamic and aeroacoustic characteristics of multirotor electric vertical take-off and landing aircraft in hovering and forward flight are compared using two representative control methods: fixed-pitch control and variable-pitch control (i.e., revolution per minute (RPM) control and collective pitch control). To this end, a comprehensive multirotor noise assessment framework is used to predict the aerodynamics and noise of multirotors controlled by RPM and collective pitch. High-resolution time–frequency analysis is used to clarify the differences in noise spectral characteristics. The findings indicate that blade passage frequency characteristics vary significantly depending on the control method. The phase differences between the rotors of variable-pitch control remain constant irrespective of disturbances or attitude control. In other words, the frequencies of the tone noise do not exhibit short-periodic modulation, unlike those of RPM-controlled multirotor. Additionally, the aeroacoustic characteristics of fixed-pitch and variable-pitch controlled multirotor in response to wind velocity fluctuation are investigated by considering the direction of noise radiation and interference effects. The impact of the turbulent kinetic energy of wind on the noise characteristics is examined by quantifying the noise components associated with aerodynamic unsteadiness in flight. Furthermore, the rotor phase control method can be applied to reduce noise in both control methods. The optimization of rotor phases reduces tone noise significantly at the target observer point without causing aerodynamic losses in variable-pitch controlled multirotor. In contrast, the reduction in tone noise remains relatively limited for fixed-pitch controlled multirotor, as the rotor phase differences are not maintained.

Rotating 3D laser mapping system for multi-rotor drones

Accurate positional estimation is an essential prerequisite for the regular operation of an autonomous rotary-wing Unmanned Aerial Vehicles (UAV). However, the field of view (FOV) limitation problem of lidar makes it more challenging to locate the rotary-wing UAV in an unknown environment. To address rotor drones with an insufficient FOV and the observation blindness of lidar in complex environments, this paper designs a rotorcraft UAV system based on rotating 3D lidar and proposes a simultaneous localization and mapping algorithm for rotating 3D lidar. The algorithm distinguishes between planar and edge features based on the curvature value of the point cloud first. Then, to reduce the impact caused by the UAV motion and lidar rotation, messages about the Inertial Measurement Unit (IMU) and real-time rotation angles are used to compensate for these motions twice, while the IMU measurements are used for state prediction, and the error-sate iterative extended Kalman filter is used to update the residuals after matching line and surface features with sub-map. Finally, Smoother high-rate odometer data was obtained through IMU pre-integration and a first-order low-pass filter. The experimental results show that the proposed rotating lidar unit in indoor and outdoor conditions makes the rotorcraft UAV have a larger FOV, which not only improves the environmental perception capability and positional estimation accuracy of the rotorcraft but enhances the positioning reliability and flight stability of the rotorcraft UAV in complex environments.

The acoustics of multirotor platforms for urban air mobility

The sound field of hovering rotors has generated tremendous interest in recent years with the prospect of urban air mobility platforms comprising eVTOL type designs. In this presentation, we will review a number of key advancements that have been made with regard to an understanding of the sound field produced by multirotor systems in hover and how it relates to the aerodynamic performance of the rotor. Much of this work is based on laboratory-scale measurements of different configurations starting with multirotor drones comprising quad-copter, hexacopter, and even octocopter arrangements (Tinney and Sirohi, AIAA J., 2018) followed by stacked corotating rotors (Valdez and Tinney, AIAA J., 2022) where the trade-space based on stacking distance, index and angle and rotor speed are evaluated. Of particular interest is the constructive and destructive interference of sound waves from neighboring rotors which augment the first few fundamental rotor harmonics. Methods for characterizing the first few rotor harmonics based on POD-based Vold-Kalman order tracking filters (Tinney etal., Int. J. Aeroacoust., 2023) is presented as well as methods for collapsing the high frequency rotor broadband noise using a moving source model (Tinney et al., AIAA Paper 2023-3222).

Drones for litter monitoring on coasts and rivers: suitable flight altitude and image resolution

Multirotor drones can be efficiently used to monitor macro-litter in coastal and riverine environments. Litter on beaches, dunes and riverbanks, along with floating litter on coastal and river waters, can be spotted and mapped from aerial drone images. Items detection and classification are prone to image resolution, which is expressed in terms of Ground Sampling Distance (GSD). The GSD is determined by drone flight altitude and camera properties. This paper investigates what is a suitable GSD value for litter survey. Drone flight altitude and camera setup should be chosen to obtain a GSD between 0.5 cm/px and 1.25 cm/px. Within this range, the lowest GSD allows litter categorization and classification, whereas the highest value should be adopted for a coarser litter census. In the vision of drawing up a global protocol for drone-based litter surveys, this work sets the ground for homogenizing data collection and litter assessments.

A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones

Innovative drone-based technologies provide novel techniques to guarantee the safety and quality of power supply and to perform these tasks more efficiently. Electric multirotor drones, which are at the forefront of technology, face significant flight time limitations due to battery capacity and weight constraints that limit their autonomous operation. This paper presents a novel drone charging station that harvests energy from the magnetic field present in power lines to charge the drone’s battery. This approach relies on a charging station that is easy to install by the drone on an overhead AC power line without modifying the electrical infrastructure. This paper analyses the inductive coupling between the energy harvester and the power line, electrical protection, the power electronics required for maximum power point tracking and the mechanical design of the charging station. A drone that perches on a cable, an end effector for installation procedures and the charging maneuver are described, along with discussion of the robotic and electrical tests performed in a relevant environment. Finally, a lightweight drone charging station capable of harvesting 145 W of power from a 600 A line current is reported.

Trajectory Tracking of a Quadcopter UAV using PID Controller

UAVs or Drones are aircraft with no onboard pilot to control the flight. They are introduced in a few categories such as single-rotor, multi-rotors, fixed-wing, and hybrid VTOL. As for multirotor drones, quadcopters are the most well-known either commercially or in the research field. Due to its advantages, a quadcopter has been chosen to perform various tasks across various fields such as entertainment, military, meteorological reconnaissance, civil and emergency responses. As the demand for quadcopters has diverged, the required features of quadcopters have also diverged. One of the current features required by quadcopters is the ability to track trajectories. However, due to its nature of non-linearity, under-actuated and unstable, controlling quadcopter for an accurate and stable performance is quite a challenge. Despite the various proposed methods throughout the past decades, PID controller is still used as either the main controller or the base controller in most cases of industrial control, including quadcopter, mainly due to its simplicity and robustness. However, to design a proper PID controller for quadcopter system is a challenge as it defies the control inputs of four with its six degree-of-freedom form, in which six inputs are required to be controlled to ensure a stable and accurate flight. This paper derived a mathematically model of a quadcopter with Newton-Euler’s equation. Some assumptions on the body and structure of the quadcopter are taken into account to make the modelling possible. Then, a manually tuned PID controller is designed to achieve the objective of controlling the operation and stability of the quadcopter during its flight. The designed controller is tested with five different trajectories which are circular, square, lemniscate, zigzag, and spiral. The results show the proposed controller successfully tracks the desired trajectories, which prove PID controller can be used to control a quadcopter.

Flight Experiments and Numerical Simulations for Investigating Multicopter Flow Field and Structure Deformation

The flow field induced by multirotor drones is of high interest for atmospheric research, as it locally influences the atmosphere and therefore may have an impact on the sensors installed for atmospheric measurements. Further, on-board vibrations can cause significant interference with the measurement equipment. To investigate the near flow field, an approach combining measurements of pressure and temperature distribution in-flight and in a laboratory setup together with numerical simulations was applied. Existing high-frequency measurement equipment was piggybacked during the initial flight tests with a newly developed 25 kg quadcopter system in a low-cost early-stage-error approach to obtain initial data and experience. During the flights, high resolution sensors for measuring pressure, temperature, acceleration, and deformation were applied with different setups at different locations below one of the rotor planes, respectively, at one rotor arm, to determine the multicopter’s influence on pressure and temperature measurements, to investigate rotor arm deformations, and to obtain data to compare with numerical simulations of this rotor setup. An external Schlieren-type measurement technique was tested to visualise the rotor vortices. The applied measurement techniques proved to be suitable for acquiring the state of the rotor-induced flow, but with some limitations. The comparison of measurements and simulations showed basic agreement and allowed for the identification of necessary adaptations for subsequent studies. The interaction of the rotor wakes with the rotor arms could be identified as the main source of the measured structural vibrations. The need for necessary improvements in the measurement setup, flight operation, and simulation setup is presented in detail.

Dispersal of blackbird flocks from sunflower fields: efficacy influenced by flock and field size but not drone platform

AbstractCrop 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.

Mode-Switching Algorithm to Improve Variable-Pitch-Propeller Thrust Generation for Drones Under Motor Current Limitation

Research and development are active in multirotor drones. Attention has been focused on improving drone mobility performance by introducing variable-pitch propellers. However, previous studies have not considered main motor currents in their controller design. The aim of this study is to improve thrust response by controlling the variable-pitch propeller in the thrust dimension while keeping steady-state efficiency. Feed-forward control of thrust by switching modes using the maximum current was designed. The control is designed to switch between a thrust-reaching mode that uses pitch angle and rotational speed and an efficiency-optimizing mode. The mode-switching transitions were verified by simulation, and the effectiveness of the proposal was experimentally validated.

A novel approach to control four multi-rotor drones in cooperative paired control using relative Jacobian

AbstractThis work presents a new formulation to holistically control four cooperative multi-rotor drones controlled in two pairs. This approach uses a modular relative Jacobian with components consisting of the Jacobians of each individual drone. This type of controller relies mainly on the relative motion between the drones, consequently releasing unnecessary constraints inherent to the control of drones in absolute motion. We present the derivations of all the necessary equations of the modular relative Jacobian to control the four multi-rotor drones. We also present the derivations of the Jacobian for each drone. We implement our proposed method in the Gazebo RotorS simulation using four hexa-rotor drones modeled from Ascending Technologies called firefly drones. We present the simulation results and analyze them to show the effectiveness of our proposed approach.

Geometric Primitive-Guided UAV Path Planning for High-Quality Image-Based Reconstruction

Image-based refined 3D reconstruction relies on high-resolution and multi-angle images of scenes. The assistance of multi-rotor drones and gimbal provides great convenience for image acquisition. However, capturing images with manual control generally takes a long time. It could easily lead to redundant or insufficient local area coverage, resulting in poor quality of the reconstructed model. We propose a surface geometric primitive-guided UAV path planning method (SGP-G) that aims to automatically and quickly plan a collision-free path to capture fewer images, based on which high-quality models can be obtained. The geometric primitives are extracted by plane segmentation on the proxy, which performs three main functions. First, a more representative evaluation of the reconstructability of the whole scene is realized. Second, two optimization strategies for different geometric primitives are executed to quickly generate a near-global optimized set of viewpoints. Third, regularly arranged viewpoints are generated to improve the efficiency of image acquisition. Experiments on virtual and real scenes demonstrate the remarkable performance of our method. Compared with the state of the art, we accomplish the planning of the photographic path with higher efficiency in a relatively simple way, achieving equivalent and even higher quality of the reconstructed model with fewer images.

Real-time propeller fault detection for multirotor drones based on vibration data analysis

This article presents a Fault Detection (FD) method to deal with propeller faults on multirotor drones in real-time. Several solutions have been proposed in the literature, however, they depend on additional sensors and/or dedicated hardware to deal with heavy computational complexity. So, they cannot be implemented in off-the-shelf commercial devices, i.e., without the aid of additional on-board sensors and/or extra computational power. The proposed method, instead, requires the on-board Inertial Measurement Unit (IMU) data only: by combining Finite Impulse Response (FIR), together with sparse classifiers, only a subset of the features is actually needed online and the FD is thus feasible in real-time.Design and tests are based on real flight data from a hexarotor, equipped with a conventional ArduPilot-based controller. The classification accuracy in testing is up to 93.37% (98.21%) with a binary tree (Linear Support Vector Machine (LSVM)). Moreover, the space and time complexity of the proposed method is low: on a PixHawk Cube flight controller, it requires less than 2% of the cycle time, and can then run in real-time. Finally, the proposed fault detection solution is model-free and it can be easily generalized to other multirotor vehicles.

Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types

In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar.

Autonomous Recharging and Flight Mission Planning for Battery-Operated Autonomous Drones

Unmanned aerial vehicles (UAVs), commonly known as drones, are being increasingly deployed throughout the globe as a means to streamline monitoring, inspection, mapping, and logistic routines. When dispatched on autonomous missions, drones require an intelligent decision-making system for trajectory planning and tour optimization. Given the limited capacity of their onboard batteries, a key design challenge is to ensure the underlying algorithms can efficiently optimize the mission objectives along with recharging operations during long-haul flights. With this in view, the present work undertakes a comprehensive study on automated tour management systems for an energy-constrained drone: (1) We construct a machine learning model that estimates the energy expenditure of typical multi-rotor drones while accounting for real-world aspects and extrinsic meteorological factors. (2) Leveraging this model, the joint program of flight mission planning and recharging optimization is formulated as a multi-criteria Asymmetric Traveling Salesman Problem (ATSP), wherein a drone seeks for the time-optimal energy-feasible tour that visits all the target sites and refuels whenever necessary. (3) We devise an efficient approximation algorithm with provable worst-case performance guarantees and implement it in a drone management system, which supports real-time flight path tracking and re- computation in dynamic environments. (4) The effectiveness and practicality of the proposed approach are validated through extensive numerical simulations as well as real-world experiments. Note to Practitioners—This study is stimulated by the need for developing pragmatic and provably efficient automated tour management systems for UAVs deployed on energy-constrained, long-distance flight missions. As such, UAVs provide a nifty platform for facilitating environmental monitoring, disaster management, transport of medical supplies, as well as expediting last-mile deliveries. However, existing path planners generally fall short of capturing several crucial aspects, such as detailed power consumption model (e.g., factoring in payload, wind speed and direction) or performance guarantees, potentially leading to underutilized or infeasible routing decisions. To address these issues, the present work proposes a theoretically-backed routing approach with a certifiable degree of optimality and develops an effective, practical power consumption evaluation model for multi-rotor UAVs, verified on multiple drone models.