Commercial Drone Research Articles

Experimental assessment of the noise characteristics of propellers for commercial drones

Multi-copters or drones are engaged in a wide range of industrial applications for their flexibility, safety and low-cost. The noise emission is becoming an issue with the expanding applications, among which the propellers that drive the drones are the major sources of noise. In this work, the noise characteristics of small-scale propellers is experimentally investigated using the advanced rotor aerodynamics and aeroacoustics test platform in an anechoic chamber at the Hong Kong University of Science and Technology (HKUST). The study will focus on the representative off-the-shelf propellers. The rotor noise will be measured by a linear array with 20 microphones, and the aerodynamic forces will be acquired by using the high-accuracy load cells. The dependence of both the tonal and broadband noise radiation with the thrust and rotation speed at various conditions will be tested. The study will enhance our understanding of the noise features of the multi-rotor powered drones, and will provide us with a better understanding of the status of the drone noise impact on the environment.

The behavioral responses of a nocturnal burrowing marsupial (Lasiorhinus latifrons) to drone flight.

The use of drones in wildlife research and management is increasing. Recent evidence has demonstrated the impact of drones on animal behavior, but the response of nocturnal animals to drone flight remains unknown. Utilizing a lightweight commercial drone, the behavioral response of southern hairy‐nosed wombats (Lasiorhinus latifrons) to drone flights was observed at Kooloola Station, Swan Reach, South Australia. All wombats flown over during both day and night flights responded behaviorally to the presence of drones. The response differed based on time of day. The most common night‐time behavior elicited by drone flight was retreat, compared to stationary alertness behavior observed for daytime drone flights. The behavioral response of the wombats increased as flight altitude decreased. The marked difference of behavior between day and night indicates that this has implications for studies using drones. The behavior observed during flights was altered due to the presence of the drone, and therefore, shrewd study design is important (i.e., acclimation period to drone flight). Considering the sensory adaptations of the target species and how this may impact its behavioral response when flying at night is essential.

Visuospatial Working Memory for Autonomous UAVs: A Bio-Inspired Computational Model

Visuospatial working memory is a fundamental cognitive capability of human beings needed for exploring the visual environment. This cognitive function is responsible for creating visuospatial maps, which are useful for maintaining a coherent and continuous representation of visual and spatial relationships among objects present in the external world. A bio-inspired computational model of Visuospatial Working Memory (VSWM) is proposed in this paper to endow Autonomous Unmanned Aerial Vehicles (UAVs) with this cognitive function. The VSWM model was implemented on a low-cost commercial drone. A total of 30 test cases were designed and executed. These test cases were grouped into three scenarios: (i) environments with static and dynamic vehicles, (ii) environments with people, and (iii) environments with people and vehicles. The visuospatial ability of the VSWM model was measured in terms of the ability to classify and locate objects in the environment. The VSWM model was capable of maintaining a coherent and continuous representation of visual and spatial relationships among interest objects presented in the environment even when a visual stimulus is lost because of a total occlusion. The VSWM model proposed in this paper represents a step towards autonomous UAVs capable of forming visuospatial mental imagery in realistic environments.

Weather constraints on global drone flyability

Small aerial drones are used in a growing number of commercial applications. However, drones cannot fly in all weather, which impacts their reliability for time-sensitive operations. The magnitude and global variability of weather impact is poorly understood. We explore weather-limited drone flyability (the proportion of time drones can fly safely) by comparing historical wind speed, temperature, and precipitation data to manufacturer-reported thresholds of common commercial and weather-resistant drones with a computer simulation. We show that global flyability is highest in warm and dry continental regions and lowest over oceans and at high latitudes. Median global flyability for common drones is low: 5.7 h/day or 2.0 h/day if restricted to daylight hours. Weather-resistant drones have higher flyability (20.4 and 12.3 h/day, respectively). While these estimates do not consider all weather conditions, results suggest that improvements to weather resistance can increase flyability. An inverse analysis for major population centres shows the largest flyability gains for common drones can be achieved by increasing maximum wind speed and precipitation thresholds from 10 to 15 m/s and 0–1 mm/h, respectively.

Drone GPS data analysis for flight path reconstruction: A study on DJI, Parrot & Yuneec make drones

In past few years, the technological growth in field of drones has not only offered multiple opportunities, but has also thrown numerous threats and challenges. Drone is a potential weapon in itself and can be used to carry out a targeted attack, carry out reconnaissance, smuggling of arms/drugs, etc. Due to cheap and easy availability of commercial drones in the market, the security threat posed by the drones has increased manifold. Additionally, the possibility of customization and option of assembling a drone by an individual makes it an even bigger threat. Unfortunately it is difficult to completely restrict/stop use of such aerial platforms by malicious actors. Therefore there is a need to develop techniques and procedures in the field of drone forensics to be able to extract and accurately analyse the positioning data, provide valuable tactical information about flight take-off location, flight path taken and reconstruct the flight operation, if required. The study in this paper aims at extracting and analysing GPS data from three different drone models belonging to three different families, followed by representing the positioning data as flight path. This paper also introduces a newly developed utility tool named “FlyLog Converter Tool” which processes and converts the.txt/.json flight log of Parrot make drones into easily understandable.csv format.

Fewer bat passes are detected during small, commercial drone flights

Advances in technological capabilities, operational simplicity and cost efficiency have promoted the rapid integration of unmanned aerial vehicles (UAVs) into ecological research, providing access to study taxa that are otherwise difficult to survey, such as bats. Many bat species are currently at risk, but accurately surveying populations is challenging for species that do not roost in large aggregations. Acoustic recorders attached to UAVs provide an opportunity to survey bats in challenging habitats. However, UAVs may alter bat behaviour, leading to avoidance of the UAV, reduced detection rates and inaccurate surveys. We evaluated the number of bat passes detected with and without the presence of a small, commercial UAV in open habitats. Only 22% of bat passes were recorded in the presence of the UAV (0.23 ± 0.09 passes/min) compared to control periods without the UAV (1.03 ± 0.17 passes/min), but the effect was smaller on the big brown bat/silver-haired bat (Eptesicus fuscus/Lasionycteris noctivagans) acoustic complex. Noise interference from the UAV also reduced on-board bat detection rates. We conclude that acoustic records attached to UAVs may inaccurately survey bat populations due to low and variable detection rates by such recorders.

Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy

Weed management is a crucial issue in agriculture, resulting in environmental in-field and off-field impacts. Within Agriculture 4.0, adoption of UASs combined with spatially explicit approaches may drastically reduce doses of herbicides, increasing sustainability in weed management. However, Agriculture 4.0 technologies are barely adopted in small-medium size farms. Recently, small and low-cost UASs, together with open-source software packages, may represent a low-cost spatially explicit system to map weed distribution in crop fields. The general aim is to map weed distribution by a low-cost UASs and a replicable workflow, completely based on open GIS software and algorithms: OpenDroneMap, QGIS, SAGA and OpenCV classification algorithms. Specific objectives are: (i) testing a low-cost UAS for weed mapping; (ii) assessing open-source packages for semi-automatic weed classification; (iii) performing a sustainable management scenario by prescription maps. Results showed high performances along the whole process: in orthomosaic generation at very high spatial resolution (0.01 m/pixel), in testing weed detection (Matthews Correlation Coefficient: 0.67–0.74), and in the production of prescription maps, reducing herbicide treatment to only 3.47% of the entire field. This study reveals the feasibility of low-cost UASs combined with open-source software, enabling a spatially explicit approach for weed management in small-medium size farmlands.

Modelling and mitigation of drone noise

According to studies by Airbus, one of the biggest concerns among the public regarding drones is the noise they produce. ICAO have also stated that when drones are flown “low and in great numbers” they will cause a significant level of noise. To date there is literature around the public’s perception of drone usage and noise, however there is currently no recommended software package for modelling drone noise. There has also been little work done into the level of noise that is likely to be emitted by drones used in the commercial market for tasks such as parcel delivery. This research aimed to investigate the noise emitted by drones and determine suitable operating conditions and flight paths to mitigate this noise. The research also aimed to investigate the suitability of the software package ‘iNoise’ for modelling noise emission by drones. The results showed noise emission can be mitigated by maintaining high altitudes and high speeds, as well as vertical take offs and landings. Although many of the noise levels recorded in this research for different flight conditions and flow numbers were lower than the maximum recommended LDEN for road traffic noise, they did approach this value at low speeds. As the sound power level used in this project came from a small quadcopter, it is reasonable to assume larger, commercial drones will have a higher sound power level, thus increasing the need for noise mitigation measures. The research also demonstrated the suitability of iNoise for modelling noise emissions caused by drone activity.

Multispectral aerial imagery-based 3D digitisation, segmentation and annotation of large scale urban areas of significant cultural value

• Urban areas 3D digitisation using multispectral aerial imagery • Experimental low-cost multispectral camera for drone development • Supervised machine learning methods for 3D mesh segmentation using multispectral data • Performance evaluation of the proposed methods on a large scale case study • Web-based visualisation and annotation tool for segmentation fine-tuning Disaster risk management of movable and immovable cultural heritage is a highly significant research topic. In this work, we present a pipeline for 3D digitisation, segmentation and annotation of large scale urban areas in order to produce data that can be exploited in disaster management simulators (e.g fire spreading, crowd movement, firefighting training, evacuation planning, etc.). We have selected the old town of Xanthi (Greece) as a challenging case study. We developed a custom multispectral camera to be carried by a commercial drone. Using the structure from motion / multiview stereo (SFM/MVS) approach, we produced a 3D model of the urban area covering 0.5 k m 2 that is followed by a multilayer texture map which carries information from visible and near-infrared regions of the electromagnetic spectrum. We developed a set of machine learning approaches based on logistic regression, support vector machines and artificial neural networks that allow 3D model segmentation by exploiting not only morphological and structural features but also the multispectral behaviour of different material surfaces. We objectively evaluate the performance of the proposed segmentation approaches on six significant material-based classes (cobbled-roads granite kilns, building walls, ceramic roof-tiles, low-vegetation, high-vegetation and metal surfaces) that are used in simulating fire propagation and crowd movement. The experiments revealed that the segmentation accuracy can be enhanced by taking into consideration surface material multispectral properties as well as morphological features. A Web-based multi-user annotation tool complements our proposed pipeline by enabling further 3D model segmentation, fine tuning and semantics annotation (e.g. usage-based building classification and evacuation priorities, escape paths and gathering points).

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis.

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

Development of a Remote-Controlled Drone System by Using Only Eye Movements: Design of a Control Screen Considering Operability and Microsaccades

In recent years, the number of bedridden patients, including amyotrophic lateral sclerosis (ALS) patients, has been increasing with the aging of the population, owing to advances in medical and long-term care technology. Eye movements are physical functions that are relatively difficult to be affected, even if the symptoms of ALS progress. Focusing on this point, in this paper, in order to improve the quality of life (QOL) of bedridden patients, including ALS patients, we propose a drone system connected to the Internet that can be remotely controlled using only their eyes. In order to control the drone by using only their eyes, a control screen and an eye-tracking device were used in this system. By using this system, for example, the patients in New York can operate the drone in Kyoto using only their eyes, enjoy the scenery, and talk with people in Kyoto. In this drone system, since a time delay could occur depending on the Internet usage environment, agile operation is required for remotely controlling the drone. Therefore, we introduce the design of the control screen focused on remote control operability and human eye movements (microsaccades). Furthermore, considering the widespread future use of this system, it is desirable to use a commercial drone. Accordingly, we describe the design of a joystick control device to physically control the joysticks of various drone controllers. Finally, we present experimental results to verify the effectiveness of this system, including the control screen and the joystick control device.

Unmanned Aircraft System Traffic Management (UTM) Simulation of Drone Delivery Models in 2030 Japan

An unmanned aircraft system traffic management (UTM) system to support flights beyond visual line-of-sight is considered necessary for the promotion of commercial drone use. In the research and development of UTM systems, cost and time constraints make it difficult to actually fly a large number of drones in the same airspace, so research is mainly conducted using a simulator. This paper presents details of a UTM simulator named the “scalable simulator for knowledge of low-altitude environment” (SKALE) developed by the Japan Aerospace Exploration Agency (JAXA), with respect to the construction of a model case of drone delivery model set in 2030 in Japan. Moreover, UTM concepts for airspace safety and efficient airspace utilization (parcel transport) are proposed and evaluated using JAXA’s UTM simulator and drone delivery model cases. Simulation results are discussed, and the knowledge gained for the improvement of airspace safety and airspace utilization (parcel transport) efficiency is documented.

Utilization of UAS data by transportation agencies: building on the experience of construction contractors

Unmanned Aerial Systems (UAS) are being used on infrastructure construction sites by contractors for many different purposes; this paper discusses how these UAS activities can be used to support Departments of Transportation (DOT). The information in this paper is based on information in the literature and the results of a survey of DOT construction contractors and professionals. Representative UAS construction applications include construction progress monitoring, safety surveillance, quality assurance, documentation of work zone conditions following an incident, quantity measurement, and communication with stakeholders. The UAS data typically consists of high definition pictures and video from a standard commercial drone. This paper reviews the construction UAS applications that have been successfully used, and discusses how these directly relate to activities that are also important for DOTs such as monitoring construction activities, quality assurance, and managing the safety of the work zone and the construction site.

Geological and seafloor investigations of the cause of partial submergence of Yokoshima Island, Nagasaki, Japan

Between 1950 and 1980, about half of Yokoshima Island disappeared beneath the East China Sea. The island lies about 1 km off the coast of Nagasaki and was mined for coal during the late 19th and early 20th centuries. We analyzed the geology of the island and surrounding seafloor to investigate how it became submerged. Two low-cost techniques were used to view underwater structures: a commercial drone fitted with a video camera in waters shallower than 5 m and an echo sounder and side-scan sonar system designed for amateur fishers in waters deeper than 5 m. We showed that the partial submergence of the island could be interpreted as a result of subsidence. Subsidence and northward tilting of sedimentary rocks north of the island, probably caused by collapse of abandoned subsea mine workings, created an extensional regime south of the island that led to the formation of steep cliffs and collapse of the southern side of the island. Our study also shows that low-cost commercially available technology can be used for rapid scientific investigations in shallow water areas. • Submergence of half of an island in a 30 year period was investigated. • Low-cost shallow seafloor investigation techniques helped geological analysis. • Subsidence was associated with uneven settlement into abandoned coal mine workings.

Autonomous Mission of Multi-UAV for Optimal Area Coverage.

This study proposes an entire hardware and software architecture from operator input to motor command for the autonomous area coverage mission using multiple unmanned aerial vehicles. Despite the rapid growth of commercial drone services, there are many limitations on operations, such as a low decision-making autonomy and the need for experienced operators to intervene in the whole process. For performing the area coverage mission more efficiently and autonomously, this study newly designs an optimization problem that allocates waypoints created to cover that area to unmanned aerial vehicles. With an optimized list of waypoints, unmanned aerial vehicles can fill the given areas with their footprints in a minimal amount of time and do not overlap each other during the mission. In addition, this study performs both various simulations for quantitative analysis and an outdoor experiment through real hardware implementation in order to verify the performance sufficiently. The methodologies developed in this study could be applied to endless applications using unmanned aerial vehicles equipped with mission-specific sensors.

DronePrint

With the ubiquitous availability of drones, they are adopted benignly in multiple applications such as cinematography, surveying, and legal goods delivery. Nonetheless, they are also being used for reconnaissance, invading personal or secure spaces, harming targeted individuals, smuggling drugs and contraband, or creating public disturbances. These malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. Therefore, it is vital to identify drones in different environments to assist the decisions on whether or not to contain unknown drones. While there are several methods proposed for detecting the presence of a drone, they have limitations when it comes to low visibility, limited access, or hostile environments. In this paper, we propose DronePrint that uses drone acoustic signatures to detect the presence of a drone and identify the make and the model of the drone. We address the shortage of drone acoustic data by relying on audio components of online videos. In drone detection, we achieved 96% accuracy in a closed-set scenario, and 86% accuracy in a more challenging open-set scenario. Our proposed method of cascaded drone identification, where a drone is identified for its 'make' followed by the 'model' of the drone achieves 90% overall accuracy. In this work, we cover 13 commonly used commercial and consumer drone models, which is to the best of understanding is the most comprehensive such study to date. Finally, we demonstrate the robustness of DronePrint to drone hardware modifications, Doppler effect, varying SNR conditions, and in realistic open-set acoustic scenes.

Nozzle with a Feedback Channel for Agricultural Drones

In recent years, small drones have been used in agriculture, for spraying water and pesticides. Although spraying systems affect the efficiency of agricultural drones considerably, research on the spraying system of drones is insufficient. In this paper, a new nozzle with a feedback channel is proposed for agricultural drones. The proposed nozzle was manufactured through 3D printing, and its performance was compared with that of the nozzle used in commercial agricultural drones. Images taken with a high-speed camera were digitally processed, to track the area and location of spray particles, and the spraying characteristics were evaluated based on the size and uniformity of the droplets obtained from the images. The proposed nozzle provided a better performance, as it could spray smaller droplets more uniformly. Commercial nozzle droplets have an average diameter of 1.76 mm, and the proposed nozzle has been reduced to a maximum of 215 μm. In addition, the full width at half maximum (FWHM) of the commercial nozzle is 0.233, but the proposed nozzle is up to 1.519; the proposed nozzle provided better performance, as it could spray smaller droplets more uniformly. Under the condition of 30 kg, the best performance in the proposed nozzle, the minimum value of the average droplet diameter of the nozzle without feedback channel is 595 μm and the maximum value of FWHM is 1.329. Therefore, a comparison of the performance of the proposed nozzle with that of a nozzle with no feedback channel indicates that the feedback channel effectively reduces the droplet diameter and improves the spraying uniformity. It is expected that the proposed nozzle can be useful for designing the spraying systems of agricultural drones.

Real-Time Adaptive Intelligent Control System for Quadcopter Unmanned Aerial Vehicles With Payload Uncertainties

A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications.

Unmanned Aerial Vehicle (UAV) GPS Jamming Test by using Software Defined Radio (SDR) platform

This paper discusses the existing jamming method from previous publication and how the method is modified to accomplished the GPS jamming of Unmanned Aerial Vehicle (UAV). This paper also presents the result from the field test of UAV GPS jamming by using Software Defined Radio (SDR) and GNU Radio software. The field test is carried out to study the performance of the jamming signal produced and the effect of elevation angle on the jamming range. The test subject used in this field test is DJI Phantom 4 Pro, a commercial consumer grade drone.

Critical response: Investments in the imaginary: commercial drone speculations and relations

Critical response: Investments in the imaginary: commercial drone speculations and relations