• Title/Summary/Keyword: Autonomous Tracking UAV

Search Result 7, Processing Time 0.019 seconds

Autonomous Tracking of Micro-Sized Flying Insects Using UAV: A Preliminary Results

  • Ju, Chanyoung;Son, Hyoung Il
    • Journal of the Korean Society of Industry Convergence
    • /
    • v.23 no.2_1
    • /
    • pp.125-137
    • /
    • 2020
  • Tracking micro-sized insects is one of the challenges of protecting ecosystems and biodiversity. In this study, we propose an approach for the autonomous tracking of micro-sized flying insects, and develop an unmanned aerial vehicle (UAV)-based robotic system. The Kalman filter is applied to the received signal strength emitted from radio telemetry to estimate the position while reducing the measurement error and noise. The autonomous tracking strategy is a method in which the UAV rotates at one point to measure the signal strength and control its position in the strongest direction of the signal. We also design a system architecture comprising a tracking sensor system and a UAV system for micro-sized insects. The estimation and autonomous tracking of the target position by the proposed system are verified and evaluated through dynamic simulation. Therefore, in this study, we propose and validate a UAV-based tracking system for micro-sized flying insects, which has not been proposed in studies conducted thus far.

Design and Implementation of UAV System for Autonomous Tracking

  • Cho, Eunsung;Ryoo, Intae
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.12 no.2
    • /
    • pp.829-842
    • /
    • 2018
  • Unmanned Aerial Vehicle (UAV) is diversely utilized in our lives such as daily hobbies, specialized video image taking and disaster prevention activities. New ways of UAV application have been explored recently such as UAV-based delivery. However, most UAV systems are being utilized in a passive form such as real-time video image monitoring, filmed image ground analysis and storage. For more proactive UAV utilization, there should be higher-performance UAV and large-capacity memory than those presently utilized. Against this backdrop, this study described the general matters on proactive software platform and high-performance UAV hardware for real-time target tracking; implemented research on its design and implementation, and described its implementation method. Moreover, in its established platform, this study measured and analyzed the core-specific CPU consumption.

Tracking of ground objects using image information for autonomous rotary unmanned aerial vehicles (자동 비행 소형 무인 회전익항공기의 영상정보를 이용한 지상 이동물체 추적 연구)

  • Kang, Tae-Hwa;Baek, Kwang-Yul;Mok, Sung-Hoon;Lee, Won-Suk;Lee, Dong-Jin;Lim, Seung-Han;Bang, Hyo-Choong
    • Journal of the Korean Society for Aeronautical & Space Sciences
    • /
    • v.38 no.5
    • /
    • pp.490-498
    • /
    • 2010
  • This paper presents an autonomous target tracking approach and technique for transmitting ground control station image periodically for an unmanned aerial vehicle using onboard gimbaled(pan-tilt) camera system. The miniature rotary UAV which was used in this study has a small, high-performance camera, improved target acquisition technique, and autonomous target tracking algorithm. Also in order to stabilize real-time image sequences, image stabilization algorithm was adopted. Finally the target tracking performance was verified through a real flight test.

Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

  • Kwon, Youngho;Hwang, Jun
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.14 no.2
    • /
    • pp.595-609
    • /
    • 2020
  • In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.

Standardization Trends for Operation of Unmanned Aerial Vehicles based on 5G (5G 기반 무인 비행체 운용 표준화 동향)

  • Lee, H.;Bae, J.S.;Bahng, S.J.;Lee, H.S.
    • Electronics and Telecommunications Trends
    • /
    • v.36 no.4
    • /
    • pp.13-22
    • /
    • 2021
  • Among the activities of 3GPP for operating 5G-based unmanned aerial vehicles, we introduce several use cases of UAVs in 5G mobile communication such as radio access node onboard UAV, simultaneous support data transmission for UAVs and eMBB users, autonomous UAVs controlled by AI, isolated deployment of radio access through UAV, and separation of UAV service area. From this, we further summarize 5G mobile communication requirements for UAVs, including definition and operational criteria of UAS, UAS remote identification requirements, UAS usage requirements, and performance requirements. Finally, regarding 5G mobile communication-based UAS connectivity, identification and tracking support, we discuss the 3GPP UAV architecture, seven major problems, the proposed solutions to each problem, and propose the results for future specification work.

Microscopic Traffic Parameters Estimation from UAV Video Using Multiple Object Tracking of Deep Learning-based (다중객체추적 알고리즘을 활용한 드론 항공영상 기반 미시적 교통데이터 추출)

  • Jung, Bokyung;Seo, Sunghyuk;Park, Boogi;Bae, Sanghoon
    • The Journal of The Korea Institute of Intelligent Transport Systems
    • /
    • v.20 no.5
    • /
    • pp.83-99
    • /
    • 2021
  • With the advent of the fourth industrial revolution, studies on driving management and driving strategies of autonomous vehicles are emerging. While obtaining microscopic traffic data on vehicles is essential for such research, we also see that conventional traffic data collection methods cannot collect the driving behavior of individual vehicles. In this study, UAV videos were used to collect traffic data from the viewpoint of the aerial base that is microscopic. To overcome the limitations of the related research in the literature, the micro-traffic data were estimated using the multiple object tracking of deep learning and an image registration technique. As a result, the speed obtained error rates of MAE 3.49 km/h, RMSE 4.43 km/h, and MAPE 5.18 km/h, and the traffic obtained a precision of 98.07% and a recall of 97.86%.

Design of a GCS System Supporting Vision Control of Quadrotor Drones (쿼드로터드론의 영상기반 자율비행연구를 위한 지상제어시스템 설계)

  • Ahn, Heejune;Hoang, C. Anh;Do, T. Tuan
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.41 no.10
    • /
    • pp.1247-1255
    • /
    • 2016
  • The safety and autonomous flight function of micro UAV or drones is crucial to its commercial application. The requirement of own building stable drones is still a non-trivial obstacle for researchers that want to focus on the intelligence function, such vision and navigation algorithm. The paper present a GCS using commercial drone and hardware platforms, and open source software. The system follows modular architecture and now composed of the communication, UI, image processing. Especially, lane-keeping algorithm. are designed and verified through testing at a sports stadium. The designed lane-keeping algorithm estimates drone position and heading in the lane using Hough transform for line detection, RANSAC-vanishing point algorithm for selecting the desired lines, and tracking algorithm for stability of lines. The flight of drone is controlled by 'forward', 'stop', 'clock-rotate', and 'counter-clock rotate' commands. The present implemented system can fly straight and mild curve lane at 2-3 m/s.