Design, control and implementation of a moving object target tracking using image processing

Document Type : Research Paper

Authors

1 Associate Professor, Pilot Department, Imam Ali University

2 Graduated Student, Mechanical Engineering Department, Amirkabir University

Abstract

In this paper, a laboratory model is designed and built to track a moving target object using image processing. Nowadays, image processing is used as an engineering tool in digital tools and in computer networks to control other industrial tools such as flying robots, and mobile robots use image processing as feedback to detect and track objects. In this paper, using image processing, the distance error between the ground robot and the flying robot is calculated and given as feedback to the controlling robot controller. Dynamic modeling was done follower robot then, the performance of three controllers PID, PID-fuzzy and linear optimization is investigated by simulation. According to the simulation, the PID controller has a shorter response time than other controllers. Finally, the aerial robot tracking the target in real-time, and the target is not far from the sight of the aerial robot.

Keywords

References

[1] Wolfe S, Givigi S, Rabbath C-A. Cooperative Multi Model State Estimation and Control for Target Tracking UAVs. 2020.
[2] Li KW, Jia H, Peng L, Gan L. Line-of-sight in operating a small unmanned aerial vehicle: How far can a quadcopter fly in line-of-sight? Applied ergonomics. 2019;81:102898.
[3] Krajník T, Vonásek V, Fišer D, Faigl J, editors. AR-drone as a platform for robotic research and education. International conference on research and education in robotics, Springer, 2015.
[4] Cheng H, Lin L, Zheng Z, Guan Y, Liu Z, editors. An autonomous vision-based target tracking system for rotorcraft unmanned aerial vehicles. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017.
[5] Camargo MPd. Aerial machine vision, geographical information system and hue for pattern classification in agriculture: Universidade de São Paulo, 2018.
[6] Liu Y, Wang Q, Hu H, He Y. A novel real-time moving target tracking and path planning system for a quadrotor UAV in unknown unstructured outdoor scenes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(11) 2018, pp 62-72.
[7] Padhy RP, Verma S, Ahmad S, Choudhury SK, Sa PK. Deep neural network for autonomous uav navigation in indoor corridor environments. Procedia computer science. 2018, pp 43-50.
[8] Goh G, Goh G, Zhong Z. Outdoor Autonomous Landing of a Quadcopter on a Moving Platform using Off-board Computer Vision. Journal of Modeling and Optimization, 11(2), 2019, pp 86-96.
[9] Azad Zade V, Latif AM. Classification of Features Extracted from Image Foreground and Background for Tracking of Aerial Moving Targets. Tabriz journal of electrical engineering. 2016; 46(3):1-11. [Persian].
[10] Manzori A, Vossoughi G. Cooperative Control of a Quadrotor and a Mobile Robot: Sharif University, 2016.
[11] sutude Bahraini M, Taghirad H. Simultaneous Localization and Mapping of a Mobile Robot Using Robust and Adaptive Filters in Dynamical Environments: Yazd University, 2017.
[12] Rezaei Nam N, Sharafi S, Farajian N. Track moving targets using a maximum learning machine strap.  National Conference on the Application of New Technologies in Science and Engineering, Electrical and Computer and IT, 2016.
[13] Navab Zadeh MA. Simulation of robot movement to accurately track a moving target based on artificial intelligence: Bahonar University, 2018.
[14] Guevarra GEC, Moreno JNB, Reccion JCB, Sy CMO, del Rosario JRB. Development of a Quadrotor with Vision-based Target Detection for Autonomous Landing. Journal of Telecommunication, Electronic and Computer Engineering, 2018, pp 41-45.
[15] Rabah M, Rohan A, Mohamed SA, Kim S-H. Autonomous moving target-tracking for a UAV quadcopter based on fuzzy-PI. IEEE Access, 2019.
[16] Parikh A, Kamalapurkar R, Dixon WE. Target tracking in the presence of intermittent measurements via motion model learning. IEEE Transactions on Robotics, 34(3), 2018, pp 805-819.
[17] Cai Y, Xi Q, Xing X, Gui H, Liu Q, Path planning for UAV tracking target based on improved A-star algorithm. 2019 1st International Conference on Industrial Artificial Intelligence (IAI), 2019.
[18] Wang S, Jiang F, Zhang B, Ma R, Hao Q. Development of UAV-Based Target Tracking and Recognition Systems. IEEE Transactions on Intelligent Transportation Systems, 2019.
[19] Z.-Y. Zhao, M. Tomizuka, and S. Isaka, Fuzzy gain scheduling of PID controllers. IEEE transactions on systems, man, and cybernetics, vol. 23, no. 5, pp. 1392-1398, 1993.
Aerospace Knowledge and Technology Journal
Volume 9, Issue 2 - Serial Number 2
October 2020
Pages 165-175

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