Drone Movement Research Articles

New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing.

In the last years, the commercial drone/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection.

High Accuracy and Short Delay 1ch-SSVEP Quadcopter-BMI Using Deep Learning

This study considers a brain-machine interface (BMI) system based on the steady state visually evoked potential (SSVEP) for controlling quadcopters using electroencephalography (EEG) signals. An EEG channel with a single dry electrode, i.e., without conductive gel or paste, was utilized to minimize the load on users. Convolutional neural network (CNN) and long short-term memory (LSTM) models, both of which have received significant research attention, were used to classify the EEG data obtained for flickers from multi-flicker screens at five different frequencies, with each flicker corresponding to a drone movement, viz., takeoff, forward and sideways movements, and landing. The subjects of the experiment were seven healthy men. Results indicate a high accuracy of 97% with the LSTM model for a 2 s segment used as the unit of processing. High accuracy of 93% for 0.5 s segment as a unit of processing can remain in the LSTM classification, consequently decreasing the delay of the system that may be required for safety reasons in real-time applications. A system demonstration was undertaken with 2 out of 7 subjects controlling the quadcopter and monitoring movements such as takeoff, forward motion, and landing, which showed a success rate of 90% on average.

Distributed Joint Power, Association and Flight Control for Massive-MIMO Self-Organizing Flying Drones

This article studies distributed algorithms to control self-organizing flying drones with massive MIMO networking capabilities - a network scenario referred to as mDroneNet . We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile hotspots mounted on drones and endowed with a large number of antennas; when we can control the position of the drone hotspots, the association between the ground users and the drone hotspots, as well as the pilot sequence assignment and transmit power for the ground users? To the best of our knowledge, this is the first time that massive MIMO capabilities are considered in self-organizing flying drone networks. We first derive a mathematical formulation of the problem of joint power, association and movement control in mDroneNet, with the objective of maximizing the aggregate spectral efficiency of the ground users. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming (MINLP) problem. Then, a distributed solution algorithm with polynomial time complexity is designed by solving three closely-coupled subproblems: access association, joint pilot sequence assignment and power control, and drone movement control. As a performance benchmark, a globally-optimal but centralized solution algorithm is also designed based on a combination of the branch and bound framework and convex relaxation techniques. Results indicate that the distributed solution algorithm converges fast (within tens of iterations) and achieves a network spectral efficiency very close to the global optimum obtained by the centralized solution algorithm (over 90% in average).

Fundamental Elements of an Urban UTM

Urban airspace environments present exciting new opportunities for delivering drone services to an increasingly large global market, including: information gathering; package delivery; air-taxi services. A key challenge is how to model airspace environments over densely populated urban spaces, coupled with the design and development of scalable traffic management systems that may need to handle potentially hundreds to thousands of drone movements per hour. This paper explores the background to Urban unmanned traffic management (UTM), examining high-level initiatives, such as the USA’s Unmanned Air Traffic (UTM) systems and Europe’s U-Space services, as well as a number of contemporary research activities in this area. The main body of the paper describes the initial research outputs of the U-Flyte R&D group, based at Maynooth University in Ireland, who have focused on developing an integrated approach to airspace modelling and traffic management platforms for operating large drone fleets over urban environments. This work proposes pragmatic and innovative approaches to expedite the roll-out of these much-needed urban UTM solutions. These approaches include the certification of drones for urban operation, the adoption of a collaborative and democratic approach to designing urban airspace, the development of a scalable traffic management and the replacement of direct human involvement in operating drones and coordinating drone traffic with machines. The key fundamental elements of airspace architecture and traffic management for busy drone operations in urban environments are described together with initial UTM performance results from simulation studies.

DESIGN WAYPOINT DRONE HEXACOPTER MODEL USING SUGENO FUZZY LOGIC

Fuzzy logic is a scientific branch of Artificial Intelligence that was developed and applied to machines to be able to approach human intelligence. In order this machine or tool to be able to execute commands in a "more humane" manner, this AI uses a rule base as a reference for movement which then goes into the defuzzyication process. To apply the fuzzy logic rule base on the drone movement automatically, an error value is taken at tetta (θ) which was previously processed by the Odometery Method by taking the position value of the drone with GPS. In addition, the drone is also monitored through Android in real-time so that the user can see the position and set the goals of the drone. In order for the processor and realtime process to run optimally, the drone will use Arduino Mega as a processor, control center, and read input from each sensor related to the drone before entering FireBase (Cloud Data Base). Control of the stability of the drone system in the air is obtained by processing GPS sensors with the fuzzy logic method with an error at reading of coordinate is 0,4 meter from line distance that is given and response acceleration of fuzzy to reset the drone back to the distance of 4 m/s.

Development of a Teaching Tool and Teaching-Learning Materials for Understanding Drone Movement

Development of a Teaching Tool and Teaching-Learning Materials for Understanding Drone Movement

FlyCam: Multitouch Gesture Controlled Drone Gimbal Photography

We introduce FlyCam—a novel framework—for gimbal drone camera photography. Our approach abstracts the camera and the drone into a single flying camera object so that the user does not need to think about the drone movement and camera control as two separate actions. The camera is controlled from a single mobile device with six simple touch gestures such as rotate, move forward, yaw, and pitch. The gestures are implemented as seamless commands that combine the gimbal motion with the drone movement. Moreover, we add a sigmoidal motion response that compensates for abrupt drone swinging when moving horizontally. The smooth and simple camera movement has been evaluated by user study, where we asked 20 human subjects to mimic a photograph taken from a certain location. The users used both the default two joystick control and our new touch commands. Our results show that the new interaction performed better in both intuitiveness and easiness of navigation. The users spent less time on task, and the System Usability Scale index of our FlyCam method was 75.13, which is higher than the traditional dual joystick method that scored at 67.38. Moreover, the NASA task load index also showed that our method had lower workload than the traditional method.

Empirical Path‐Loss Modeling and a RF Detection Scheme for Various Drones

This paper presents a path‐loss model based on a radio‐frequency (RF) detection scheme for various drones using 5G aerial communication over an industrial, scientific, and medical radio band (ISM band) network. We considered three communication modes of the ISM band for the channel characteristics analysis: the DJI Enhanced Spread Spectrum Technology (DESST) protocol, Wi‐Fi, and Bluetooth. The drone signal detection scheme extracts the drone signal from the environment mixed with the general signal. The drone DESST signal is identified through cross‐correlation of the received signal. The Wi‐Fi and Bluetooth signals are identified with the singular‐value decomposition (SVD) algorithm by using the hopping characteristics. General and drone Wi‐Fi signals are separated by in‐phase/quadrature (I/Q) phase analysis over the measurement time. The windowed received signal strength indicator (RSSI) moving detection (WRMD) analysis identifies the drone Bluetooth signal according to the movement of the drone. The detected drone signal is channel modeled by the horizontal distance d according to the altitude θ. Finally, they verify their model by a ray‐tracing simulation similar to the real environment. The model provides a simple and accurate prediction for designing future aerial communications systems according to changes in drone movement.

Flying Drone Base Stations for Macro Hotspots

We study a scenario where multiple drone-mounted base stations cruise freely over a macro hotspot to serve mobile users on the ground. The drone base stations move constantly and update their moving directions following our proposed mobility control algorithm. The constant movement of drones reduces the distance between the base stations and users, which in turn improves the probability of having a line of sight connection. We consider a practical user association scheme for the moving base stations, which enables user equipments to switch their serving base stations based only on the received signal strength. via extensive simulations, we demonstrate that the drone base stations moving according to our proposed algorithms can improve the average packet throughput by 82% and the 5th-percentile packet throughput by 430% compared to a baseline scenario, where drones hover over fixed locations. These improvements can be realized regardless of users’ and base stations’ density. The constant movement of the drones also helps reduce the total number of drones required to cover the macro hotspot.

Emergency Urban Search

The use of a video camera to support search immediately raises the question “for target location, what is the most effective way of presenting video camera output to a human observer (‘spotter’)”. We examine three presentation modes: (a) unprocessed video output, (b) ‘static visual presentation’ (SVP) in which a series of static views of the search area can be examined in turn while keeping up with drone movement, and (c), a novel mode called ‘Live SVP’ in which the locations sequentially captured by a camera are presented discretely in real time, thereby preserving any movement such as a person waving to attract attention. The dynamics of aerial video were modelled using game development software. The resulting videos were used to support realistic search exercises using human participants. The task attempted by each participant was the identification of lost school children in the simulated environment, using one of the video presentations described above. It was found that the new LSVP viewing mode is superior in those tested for moving targets in a low distraction environment. Another principal finding was that the density of distractors (i.e., non-target objects) had a significant influence on the success of target identification. KEY WORDS: Search, Rescue, Drone, Video, Target, Presentation

Primer reporte de un Área de Congregación de Zánganos de Apis mellifera (Hymenoptera: Apidae) de Argentina

Natural reproduction of honeybees occurs in zones called drone congregation areas (DCA), visited by drones (males) and queens of several colonies. These are open areas, protected from the wind by a high vegetation cover and are very common near an apiary. Until now, no DCA had been reported in Argentina. We first determined the flight activity of drones, watching the entrance and exit to the hives from 8:00 to 18:00. We found two peaks of flight activity: from 11:00 to 12:00 and from 15:00 to 17:30. To find a DCA, we tracked the movement of drones using helium-filled balloons and kites. Each contact point of drones in the bait was georeferenced with a GPS to determine the path to a DCA. We report the first DCA in northern Argentina, near an apiary that breeds queen bees, in Alberdi, Tucumán (ARG). This study is important because it implements a method for monitoring DCAs, which is useful to study the health and genetic structure and diversity of Apis mellifera L., as well as for apiculture, because it provides a tool for beekeepers that could help preserve regional ecotypes through reproductive planning for bees.

드론을 활용한 초소형 SAR 영상 구현 및 품질 보상 분석

최근 무인기 탑재를 위한 소형 SAR 시스템 관련연구가 활발하게 진행되고 있으나, 드론과 같은 소형 비행 플랫폼에 대한 적용 사례는 매우 드물다. 드론의 경우, 고정익 무인항공기에 비해 기상, 조종환경 등에 취약하므로 고품질의 SAR 영상을 획득하기 위해서는 매우 정밀한 요동 분석 및 오차 보상 알고리즘이 요구된다. 특히 소형 드론에서는 SAR 탑재체 무게 및 전력의 제약으로 자세 제어 및 센서 장착이 어려워 영상 품질 보장이 어려워진다. 본 연구에서는 드론에 SAR를 탑재하여 영상을 획득하는 가능성을 제시한다. 이를 위해 실제 레이다가 탑재된 드론을 사용하여 SAR 영상을 획득하고, 그 품질을 분석하였다. 드론 SAR 기하 구조 분석을 통해 드론의 요동에 의해 발생될 수 있는 위상오차를 분석하고, 불규칙한 드론 이동에 의한 왜곡을 보상함으로써 드론 SAR의 운용 가능성을 검증하였다. The use of small drone platform has become a popular topic in these days but its application for SAR operation has been little known due to the burden of the payload implementation. Drone platforms are distinguished from the conventional UAV system by the increased vulnerability to the turbulences, control-errors and poor motion stability. Consequently, sophisticated motion compensation may be required to guarantee the successful acquisition of high quality SAR imagery. Extremely limited power and mass budgets may prevent the use of additional hardwares for motion compensation and the difficulty of SAR focusing is further aggravated. In this paper, we have carried out a feasibility study of mico-SAR drone operation. We present the image acquisition results from the preliminary flight tests and a quality assessment is followed on the experimental SAR images. The in-flight motion errors derived from the unique drone movements are investigated and attempts have been made to compensate for the geometrical and phase errors caused by motions against the nominal trajectory. Finally, the successful operation of drone SAR system is validated through the focussed SAR images taken over test sites.