• Title/Summary/Keyword: Autonomous tracking

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OnBoard Vision Based Object Tracking Control Stabilization Using PID Controller

  • Mariappan, Vinayagam;Lee, Minwoo;Cho, Juphil;Cha, Jaesang
    • International Journal of Advanced Culture Technology
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    • v.4 no.4
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    • pp.81-86
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    • 2016
  • In this paper, we propose a simple and effective vision-based tracking controller design for autonomous object tracking using multicopter. The multicopter based automatic tracking system usually unstable when the object moved so the tracking process can't define the object position location exactly that means when the object moves, the system can't track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control used to improve the stability of tracking system, so that the result object tracking became more stable than before, it can be seen from error of tracking. A computer vision and control strategy is applied to detect a diverse set of moving objects on Raspberry Pi based platform and Software defined PID controller design to control Yaw, Throttle, Pitch of the multicopter in real time. Finally based series of experiment results and concluded that the PID control make the tracking system become more stable in real time.

A Study on Way-Point Tracking of AUV using State Feedback (상태 궤환을 사용한 AUV의 경우점 추적 연구)

  • Kwon, Soon-Tae;Baek, Woon-Kyung;Kang, In-Pil;Choi, Hyeung-Sik;Joo, Moon-G.
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.12
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    • pp.1266-1272
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    • 2011
  • For way-point tracking of an autonomous underwater vehicle, a state feedback controller was designed by using pole placement scheme in discrete time domain. In the controller, 4 state variables were used for regulating the depth of the vehicle in z direction, and 3 state variables, for steering the vehicle in xy plane. Assuming constant speed of AUV, we simplified the design of the way-point tracking system. The proposed controller was simulated by MATLAB/Simulink using 6 degree-of-freedom nonlinear model and its performance of way point tracking was shown to be fulfilled within 1 m, nevertheless the proposed controller is quite simple and easy to implement compared to sliding mode controller.

Vehicle Classification and Tracking based on Deep Learning (딥러닝 기반의 자동차 분류 및 추적 알고리즘)

  • Hyochang Ahn;Yong-Hwan Lee
    • Journal of the Semiconductor & Display Technology
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    • v.22 no.3
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    • pp.161-165
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    • 2023
  • One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

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Active Trajectory Tracking Control of AMR using Robust PID Tunning

  • Tae-Seok Jin
    • Journal of the Korean Society of Industry Convergence
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    • v.27 no.4_1
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    • pp.753-758
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    • 2024
  • Trajectory tracking of the AMR robot is one research for the AMR robot navigation. For the control system of the Autonomous mobile robot(AMR) being in non-honolomic system and the complex relations among the control parameters, it is d ifficult to solve the problem based on traditional mathematics model. In this paper, we presents a simple and effective way of implementing an adaptive tracking controller based on the PID for AMR robot trajectory tracking. The method uses a non-linear model of AMR robot kinematics and thus allows an accurate prediction of the future trajectories. The proposed controller has a parallel structure that consists of PID controller with a fixed gain. The control law is constructed on the basis of Lyapunov stability theory. Computer simulation for a differentially driven non-holonomic AMR robot is carried out in the velocity and orientation tracking control of the non-holonomic AMR. The simulation results of wheel type AMR robot platform show that the proposed controller is more robust than the conventional back-stepping controller to show the effectiveness of the proposed algorithm.

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)
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    • v.14 no.2
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    • pp.595-609
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    • 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.

Autonomous Vehicles as Safety and Security Agents in Real-Life Environments

  • Al-Absi, Ahmed Abdulhakim
    • International journal of advanced smart convergence
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    • v.11 no.2
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    • pp.7-12
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    • 2022
  • Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

A Study on the Development of CCTV Camera Autonomous Posture Calibration Algorithm for Simultaneous Operation of Traffic Information Collection and Monitoring (교통정보 수집 및 감시 동시운영을 위한 CCTV 카메라 자율자세 보정 알고리즘 개발에 관한 연구)

  • Jun Kyu Kim;Jun Ho Jung;Hag Yong Han;Chi Hyun SHIN
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.1
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    • pp.115-125
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    • 2023
  • This paper relates to the development of CCTV camera posture calibration algorithm that can simultaneously collect traffic information such as traffic volume and speed in the state of view of the CCTV camera set for traffic monitoring. The developed autonomous posture calibration algorithm uses vehicle recognition and tracking techniques to identify the road, and automatically determines the angle of view for the operator's traffic surveillance and traffic information collection. To verify the performance of the proposed algorithm, a CCTV installed on site was used, and the results of the angle of view automatically calculated by the autonomous posture calibration algorithm for the angle of view set for traffic surveillance and traffic information collection were compared.

Location Tracking Compensation Algorithm for Route Searching of Docent Robot in Exhibition Hall (전시장 도슨트 로봇의 경로탐색을 위한 위치추적 보정 알고리즘)

  • Jung, Moo Kyung;Lee, Dong Myung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.4
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    • pp.723-730
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    • 2015
  • In this paper, a location tracking compensation algorithm based on the Least-Squares Method ($LCA_{LSM}$) was proposed to improve the autonomous tracking efficiency for the docent robot in exhibition hall, and the performance of the $LCA_{LSM}$ is analyzed by several practical experiments. The proposed $LCA_{LSM}$ compensates the collected location coordinates for the robot using the Least-Squares Method (LSM) in order to reduce the cumulated errors that occur in the Encoder/Giro sensor (E/G) and to enhance the measured tracking accuracy rates in the autonomous tracking of the robot in exhibition hall. By experiments, it was confirmed that the average error reduction rates of the $LCA_{LSM}$ are higher as 4.85% than that of the $LCA_{KF}$ in Scenario 1 (S1) and Scenario 2 (S2), respectively on the location tracking. In addition, it was also confirmed that the standard deviation in the measured errors of the $LCA_{LSM}$ are much more low and constant compared to that of the E/G sensor and the $LCA_{KF}$ in S1 and S2 respectively. Finally, we see that the suggested $LCA_{LSM}$ can execute more the stabilized location tracking than the E/G sensors and the $LCA_{KF}$ on the straight lines of S1 and S2 for the docent robot.

A Study on Object Tracking for Autonomous Mobile Robot using Vision Information (비젼 정보를 이용한 이동 자율로봇의 물체 추적에 관한 연구)

  • Kang, Jin-Gu;Lee, Jang-Myung
    • Journal of the Korea Society of Computer and Information
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    • v.13 no.2
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    • pp.235-242
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    • 2008
  • An Autonomous mobile robot is a very useful system to achieve various tasks in dangerous environment, because it has the higher performance than a fixed base manipulator in terms of its operational workspace size as well as efficiency. A method for estimating the position of an object in the Cartesian coordinate system based upon the geometrical relationship between the image captured by 2-DOF active camera mounted on mobile robot and the real object, is proposed. With this position estimation, a method of determining an optimal path for the autonomous mobile robot from the current position to the position of object estimated by the image information using homogeneous matrices. Finally, the corresponding joint parameters to make the desired displacement are calculated to capture the object through the control of a mobile robot. The effectiveness of proposed method is demonstrated by the simulation and real experiments using the autonomous mobile robot.

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Acoustic theory application in ultra short baseline system for tracking AUV

  • Ji, Daxiong;Liu, Jian;Zheng, Rong
    • Ocean Systems Engineering
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    • v.3 no.1
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    • pp.71-77
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    • 2013
  • The effective tracking area of ultra short baseline (USBL) systems strongly relates to the safety of autonomous underwater vehicles (AUVs). This problem has not been studied previously. A method for determining the effective tracking area using acoustic theory is proposed. Ray acoustic equations are used to draw rays which ascertain the effective space. The sonar equation is established in order to discover the available range of the USBL system and the background noise level using sonar characteristics. The available range defines a hemisphere like enclosure. The overlap of the effective space with the hemisphere is the effective area for USBL systems tracking AUVs. Lake and sea trials show the proposed method's validity.