Multi-rotor Drone Research Articles

Commercial Multirotor UAV Campaign on Data Acquisition for Disaster Management

UAV usage and interest have shown great popularity recently, especially for commercial multirotor. It is cheap, easy to use, flexible, and has many capabilities. That is why multirotor is widely chosen in disaster management. As a technology, it possesses certain aspects. Review of how the implementation of multirotor UAV in disaster management is important to gain improvement in the technology and evaluate at how we use it. This paper provides insight into commercial multirotor UAV campaign in disaster management through literature study and personal experience in a related theme. Similar ideas on the development of UAV and its application in disaster management then combine with personal experience was used to identify the advantage, disadvantages, challenges, and opportunities of multirotor drone applications. The result suggested that multirotor UAV has limited capability in conducting disaster management campaigns. The weakness in deploying multirotor is related to the lack of technology which may be overcome in the future as the development in hardware and software technology.

Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations

Flight safety and reliability improvement is an important research issue in aerial applications. Multi-rotor drones are vulnerable to motor failures leading to potentially unsafe operations or collisions. Therefore, researchers are working on autonomous landing systems to safely recover and land the faulty drone in on a desired landing area. In such a case, a suitable landing zone should be detected rapidly in for emergency landing. Majority of the works related with autonomous landing utilize a marker and GPS signals to detect landing site. In this work, we propose a landing system framework that involves only the processing of images taken from the onboard camera of the vehicle. First, the objects in the image are determined by filtering and edge detection algorithm, then the most suitable landing zone is searched. The area that is free from obstacles and closest to the center of the image is defined as the most immediate and suitable landing zone. The method has been tested on 25 images taken from different heights and its performance has been evaluated in terms runtime on a single board computer and detection precision and recall values. The average measured runtime is 2.4923 seconds and 100% of precision and recall values are achieved for the images taken from 1m and 2m. The smallest precision and recall values are 79.1% and 81.2%, respectively.

A novel hybrid drone for multi-propose aerial transportation and its conceptual optimization based on surrogate approach

In recent years the Unmanned Aerial Vehicles (UAVs) are increasingly adopted in various applications of the aerial transportation, meanwhile both the two main classifications, the fixed wing and multi-rotors drones, are limited by their own drawbacks in different specialized domains. In order to improve the short duration of multi-rotor drone and the strict conditions for taking-off and landing of the fixed-wing, an innovative hybrid UAV with integral advantages of “delta+canard wing” and vertical lift-fans has been developed. Meanwhile, the parametrial optimization with multi-objects are discussed based on surrogate model to determine the conception of the delta wing and canard wing, and only the design speed of cruise model is considered, thus excluding the lift fan. Firstly, several parameters are chosen according to precedent experiences and researches, together with the optimal objects and constraint conditions determined by design conditions which are concluded into the typical multi-objective optimal problem; secondly, with the aid of computational fluid dynamics (CFD) limited design points generated by meta-model will make up the design of experiment (DOE) to create the response surfaces by means of Kriging method; lastly, the adaptation of the multi-objective genetic algorithm(MOGA) integrating in modeFRONTIER helps to reach the heuristic approach optimization for this problem. Therefore, the final conception of the delta wing and canard wing is achieved to perform the cruise model of the hybrid UAV.

Stochastic analysis of a single-rotor to quantify the effect of RPS variation on noise of hovering multirotors

Multirotor drone generally controls its attitude and position through the variation of the angular speed of each rotor, which makes the sound signature from it considerably different from the one of single-rotor flight vehicle. Specifically, the sound spectrum of single-rotor vehicles is dominated by the blade passing frequency (BPF) components, determined by the angular speed of the rotor. In contrast, the variation in the angular speed in the multirotor makes it challenging to predict the effects of the multirotor noise. In this study, the effects of the variation in the angular speed of each rotor on the noise of a hovering multirotor were quantified and predicted through uncertainty quantification and single-rotor stochastic analysis. Multirotor experimental results indicated that the Revolution per second (RPS) uncertainty of the multirotor is sensitive to maximum tilt angle and payload. The experimentally quantified angular speed variation is used as input to single-rotor stochastic analysis, and the measured aerodynamic and acoustics results show good agreement with the analytically/numerically predicted results. On comparing multirotor noise, the single-rotor noise with RPS uncertainty results exhibit similar spectral characteristics, especially near the BPF harmonics. Furthermore, the results reveal that the single-rotor stochastic simulation with angular speed uncertainty can predict angular speed variation effects on the multirotor noise in the hovering condition. This study concludes that the sound characteristics of a hovering multirotor can be predicted by considering a single-rotor case with RPS uncertainty.

A multi-purpose, multi-rotor drone system for long-range and high-altitude volcanic gas plume measurements

Abstract. A multi-rotor drone has been adapted for studies of volcanic gas plumes. This adaptation includes improved capacity for high-altitude and long-range, real-time SO2 concentration monitoring, long-range manual control, remotely activated bag sampling and plume speed measurement capability. The drone is capable of acting as a stable platform for various instrument configurations, including multi-component gas analysis system (MultiGAS) instruments for in situ measurements of SO2, H2S, and CO2 concentrations in the gas plume and portable differential optical absorption spectrometer (MobileDOAS) instruments for spectroscopic measurement of total SO2 emission rate, remotely controlled gas sampling in bags and sampling with gas denuders for posterior analysis on the ground of isotopic composition and halogens. The platform we present was field-tested during three campaigns in Papua New Guinea: in 2016 at Tavurvur, Bagana and Ulawun volcanoes, in 2018 at Tavurvur and Langila volcanoes and in 2019 at Tavurvur and Manam volcanoes, as well as in Mt. Etna in Italy in 2017. This paper describes the drone platform and the multiple payloads, the various measurement strategies and an algorithm to correct for different response times of MultiGAS sensors. Specifically, we emphasize the need for an adaptive flight path, together with live data transmission of a plume tracer (such as SO2 concentration) to the ground station, to ensure optimal plume interception when operating beyond the visual line of sight. We present results from a comprehensive plume characterization obtained during a field deployment at Manam volcano in May 2019. The Papua New Guinea region, and particularly Manam volcano, has not been extensively studied for volcanic gases due to its remote location, inaccessible summit region and high level of volcanic activity. We demonstrate that the combination of a multi-rotor drone with modular payloads is a versatile solution to obtain the flux and composition of volcanic plumes, even for the case of a highly active volcano with a high-altitude plume such as Manam. Drone-based measurements offer a valuable solution to volcano research and monitoring applications and provide an alternative and complementary method to ground-based and direct sampling of volcanic gases.

3D path planning and real-time collision resolution of multirotor drone operations in complex urban low-altitude airspace

Drones have been greatly developed to facilitate the progress of various industries. The safe operation of drones in the urban airspace is critical to ensure a reliable and high-efficient urban air traffic system. This work presents a fusion scheme to achieve autonomous drone collision-free path planning considering static obstacles and dynamic threats detected. Firstly, a 3D voxel jump point search (JPS) based path planning model is developed to generate the static collision-free reference path. With the optimization, the reference path is then de-diagonalized, reconstructed, and smoothed to obtain the desired path. Subsequently, a local collision resolution method is proposed to avoid near mid-air collision of the dynamic threats. The method is based on the Markov decision process (MDP) to implement real-time dynamic collision avoidance. Simulations are conducted to verify the performance of the proposed model. The simulation results demonstrate that the proposed model is effective to achieve the autonomous path planning and real-time collision resolution of multirotor drones.

Sensitivity Study of Design Parameters for a Fuel Cell Powered Multirotor Drone

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.

Reusable Surrogate Models for the Preliminary Design of Aircraft Application Systems

This paper presents a new methodology for obtaining surrogate models suitable for the preliminary design of aircraft application systems. The methodology is based on a dimensional and sensitivity analysis adapted to the needs and constraints arising from multidisciplinary design. It enables simpler and lighter models to be obtained, with satisfactory predictive accuracy over a large range of validity. These surrogate models can cover a very large area of applications, where the components may vary from very small to very large sizes, because of their broad range of validity. Avoiding the reconstruction of the model offers advantages in terms of capitalization and justifying the concept of reuse. The proposed approach is applied to generate surrogate models of a permanent magnet brushless motor used in two types of applications: one is for the preliminary design of a flight control electromechanical actuator and the other is for the preliminary design of an electric propulsion system for a multirotor drone.

Fmdtools

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved experts manually creating hazard analyses in a time-consuming process that hinders one’s ability to compare designs. Furthermore, as opposed to reliability-based design, resilience-based design requires using models to determine the dynamic effects of faults to compare recovery schemes. Models also provide design opportunities, since models can be parameterized and optimized and because the resulting hazard analyses can be updated iteratively. While many theoretical frameworks have been presented for early hazard assessment, most currently-available modelling tools are meant for the later stages of design. Given the wide adoption of Python in the broader research community, there is an opportunity to create an environment for researchers to study the resilience of different PHM technologies in the early phases of design. This paper describes fmdtools, an attempt to realize this opportunity with a set of modules which may be used to construct different design models, simulate system behaviors over a set of fault scenarios and analyze the resilience of the resulting simulation results. This approach is demonstrated in the hazard analysis and architecture design of a multi-rotor drone, showing how the toolkit enables a large number of analyses to be performed on a relatively simple model as it progresses through the early design process.

3D Reconstruction of Coastal Cliffs from Fixed-Wing and Multi-Rotor UAS: Impact of SfM-MVS Processing Parameters, Image Redundancy and Acquisition Geometry

Monitoring the dynamics of coastal cliffs is fundamental for the safety of communities, buildings, utilities, and infrastructures located near the coastline. Structure-from-Motion and Multi View Stereo (SfM-MVS) photogrammetry based on Unmanned Aerial Systems (UAS) is a flexible and cost-effective surveying technique for generating a dense 3D point cloud of the whole cliff face (from bottom to top), with high spatial and temporal resolution. In this paper, in order to generate a reproducible, reliable, precise, accurate, and dense point cloud of the cliff face, a comprehensive analysis of the SfM-MVS processing parameters, image redundancy and acquisition geometry was performed. Using two different UAS, a fixed-wing and a multi-rotor, two flight missions were executed with the aim of reconstructing the geometry of an almost vertical cliff located at the central Portuguese coast. The results indicated that optimizing the processing parameters of Agisoft Metashape can improve the 3D accuracy of the point cloud up to 2 cm. Regarding the image acquisition geometry, the high off-nadir (90°) dataset taken by the multi-rotor generated a denser and more accurate point cloud, with lesser data gaps, than that generated by the low off-nadir dataset (3°) taken by the fixed wing. Yet, it was found that reducing properly the high overlap of the image dataset acquired by the multi-rotor drone permits to get an optimal image dataset, allowing to speed up the processing time without compromising the accuracy and density of the generated point cloud. The analysis and results presented in this paper improve the knowledge required for the 3D reconstruction of coastal cliffs by UAS, providing new insights into the technical aspects needed for optimizing the monitoring surveys.

Remotely sensed identification of canopy characteristics using UAV-based imagery under unstable environmental conditions

Water is a crucial element for plant growth, metabolic processes, and general health. Water-deficit, typically simplified by drought stress, is the most critical photosynthetic source of stress that restricts plant growth, crop yield, and food product quality. This research highlights state of the art, possibilities for detecting the canopy temperature by integrating very-high-resolution RGB and thermal imagery from UAV. A multi-rotor drone has assembled by DJI (S900) attached with RGB thermal and cameras was used for experiments. The thermal cameras have a spectral range of 7.5– 13 μ m , a resolution of 640 × 512 pixels, thermal sensitivity of <0.05 °C at ＋30 °C, and a focal length of 25 mm, respectively. UAV flights were operated with DJI ground stations pro software (SZ DJI Technology Co. Ltd., China), using the DJI A3 flight controller. This work aimed to compare the accuracy of canopy temperature and to evaluate the performance of thermography. The extracted CT results were closely related to ground truth CT with the value of (R 2 ) 0.9297 and correlation (r) 0.97702, respectively. The calculated results of CWSI showed a strong relation with gs under different irrigation levels 90%–100%, 75%, 60%, and 50% of the field capacity. The relationship of each time of day was substantial with (R 2 ) 0.90, 0.75, and 0.86, respectively. The Correlation coefficients (R 2 ) of CT, stomatal conductance, and SD were compared and found to be 0.755, 0.67, and 0.695, respectively. Results stated that this approach estimates the most reliable temperature around 35 °C to 40 °C. This study demonstrates the different temperature based spectral indices and provides accurate, rapid, and reliable canopy temperature quantification. • UAV-based imagery and thermographic techniques were used for canopy temperature estimation. • Image processing and plot-level data extraction were developed. • Stitching and temperature calibration of UAV thermal images. • Remote sensing data was valuable in evaluating drought effects under unstable conditions. • The relationship of plant water stress with a data acquisition time of the day.

Derivation of high-resolution orthophoto map from multirotor drone survey for application in infrastructure management

An orthophoto is a geometrically corrected aerial image that can be used in the same approach as map. It provides various geospatial information and therefore plays an important role in infrastructure management. This paper describes the methodology of an aerial survey using a multirotor drone and the processing of aerial images acquired from the survey to produce an orthophoto map. The survey work was carried out using Zenmuse X4S photogrammetric camera controlled by an autopilot multirotor drone at 200m above the ground. Optimal flight path for the autonomous mission was created and organized using the Map Pilot software. Five ground control points were laid within the survey area and their locations were determined using a real-time kinematic global navigation satellite system. Aerial images acquired from the survey were processed with the Pix4Dmapper photogrammetry software. The photogrammetric processing generated a 6 cm-resolution orthophoto map. The presented work demonstrates the practicality of multirotor drone survey and intelligent image processing for non-professionals in deriving high-resolution orthophoto map for application in infrastructure management.

Effects of High Fidelity Modeling of Multirotor Drones

Abstract This paper examines two different factors that will affect energy consumption for multi-rotor drones with more than four rotors. First, the choice of aerodynamic model for the rotor blades is examined. Two aerodynamic models, the blade element theory (BET) model and lumped blade (LB) model, are compared using vertical, roll, pitch, and yaw trajectories. The BET and LB models produced very different trajectories with identical inputs, especially in the vertical and yaw trajectories which differed by 87.9% and 52.5%, respectively. The BET and LB models also result in different energy usages with the LB model consistently predicting 36% more energy consumption. The second factor studied is the choice of rotor groupings. For a multi-rotor drone, different rotor groupings may result in different energy usages; two groupings are considered. The same four basic trajectories are compared. The results show that the two groupings have an energy difference of 4.7–4.9% for each of the roll, pitch, and yaw directions which implies that each grouping has a base energy consumption inherent to it. Then, possible energy compounding effects are explored by examining a complex trajectory. The complex trajectory yields a 9.26% energy difference between the two groupings but further examination reveals that the difference is due to differences in the final trajectory not energy compounding effects. Thus, it is concluded that the aerodynamic model and rotor groupings are two important factors that must be considered when energy consumption needs to be minimized.

Aerodynamic noise of propeller and shroud of multi-rotor drone in cross-wind conditions

Aerodynamic noise of propeller and shroud of multi-rotor drone in cross-wind conditions

Policy forum: Proposal of a Mexican precision agroforestry policy

Agroforestry is a multifunctional and climatically smart agriculture that combines high food productivity with environmental protection according to the local sociocultural context, these advantages have prompted several countries that have developed national agroforestry initiatives, and policies. The main objective of this paper is to propose a Mexican precision agroforestry policy to promote economic growth and a resilient economy to climate change through the implementation of precision agriculture, and agroforestry 4.0 technologies for the diagnosis, monitoring, plots management, and commercialization in the Mexican agroforestry sector. The operation of the policy will be through a mobile application where the farmer will request drone monitoring services, and, with the information collected, the farmer will request multirotor drone spraying services. The Mexican government will only offer it for fifty species of trees for sixteen agroforestry systems and personal agroforestry technical advice. Additionally, the Mexican agroforestry app will have data such as weather, technical chat assistance, technical agroforestry video capsules, new agroforestry research, agroforestry input discounts, a registration platform, official information, an agroforestry offer, traceability, and home delivery. The precision agriculture tools and technologies are becoming more, and more useful in the forestry, and agroforestry sector. Mexico is the center of origin, domestication, and breeding of several agricultural species, therefor the implementation of this policy will favor its care and preservation. Mexican agroforestry policy can promote the adoption of several digital tools and technologies in other important national agroforestry policies around the world.

Suitability Analysis of Implementing a Fuel Cell on a Multirotor Drone

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.

Measuring disturbance at swift breeding colonies due to the visual aspects of a drone: a quasi-experiment study.

There is a growing body of research indicating that drones can disturb animals. However, it is usually unclear whether the disturbance is due to visual or auditory cues. Here, we examined the effect of drone flights on the behavior of great dusky swifts Cypseloides senex and white-collared swifts Streptoprocne zonaris in 2 breeding sites where drone noise was obscured by environmental noise from waterfalls and any disturbance must be largely visual. We performed 12 experimental flights with a multirotor drone at different vertical, horizontal, and diagonal distances from the colonies. From all flights, 17% caused <1% of birds to temporarily abandon the breeding site, 50% caused half to abandon, and 33% caused more than half to abandon. We found that the diagonal distance explained 98.9% of the variability of the disturbance percentage and while at distances >50 m the disturbance percentage does not exceed 20%, at <40 m the disturbance percentage increase to > 60%. We recommend that flights with a multirotor drone during the breeding period should be conducted at a distance of >50 m and that recreational flights should be discouraged or conducted at larger distances (e.g. 100 m) in nesting birds areas such as waterfalls, canyons, and caves.

Comparison of sampling precision for nearshore marine wildlife using unmanned and manned aerial surveys

Aerial surveys of large marine wildlife in nearshore areas can support management actions to ensure conservation of this megafauna. While most aerial surveys of marine wildlife have been carried out using manned aircraft, unmanned aerial systems (commonly known as drones) are being increasingly used. Here, we compare the relative accuracy and precision of marine wildlife surveys from a multirotor drone and a manned helicopter for the first time. At two locations on the east coast of Australia, we simultaneously surveyed sharks (including white sharks, Carcharodon carcharias), dolphins, rays, and sea turtles in nearshore coastal areas using a multirotor drone (DJI Inspire I) and a helicopter (Robinson 44 Clipper II) over 26 separate flights. Sampling included the real-time quantification of marine wildlife by an observer in the helicopter and the pilot of the drone. The video feed from the drone was then later re-sampled in the laboratory. Of the three methods, post-hoc analysis of drone video footage is likely to provide the most accurate and precise estimates of marine wildlife in nearshore areas. When real-time data are required (e.g., for shark-risk mitigation), manned helicopters (over larger stretches of coast) and drones (across localised beaches) will both be useful.

Predictive maintenance of photovoltaic plants using multirotor drones with automatic recharging system of Li-Po 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.

A Blind Source Separation Framework for Ego-Noise Reduction on Multi-Rotor Drones

Acoustic sensing from a multi-rotor drone is heavily degraded by the strong ego-noise produced by the rotating motors and propellers. To address this problem, we propose a blind source separation (BSS) framework that extracts a target sound from noisy multi-channel signals captured by a microphone array mounted on a drone. The proposed method addresses the challenging problem of permutation alignment, in extremely low signal-to-noise-ratio scenarios (e.g. SNR $ −15 dB), by performing clustering on the time activities of the separated signals across frequencies. Since initialization plays an important role to the success of clustering, we propose a pre-processing algorithm which uses time-frequency spatial filtering (TFS) to generate a reference to pre-align the permutation. The pre-alignment not only improves the performance of clustering and permutation alignment, but also solves the target-channel selection problem for BSS. The proposed method integrates the advantages of both TFS and BSS. Experimental results with real-recorded data show that the proposed method is capable of processing the audio stream continuously in a blockwise manner and also remarkably outperforms the state-of-the-art.