Welcome to Francis Academic Press

International Journal of Frontiers in Engineering Technology, 2021, 3(8); doi: 10.25236/IJFET.2021.030805.

Autonomous Positioning and Control System of Rotor UAV Based on Machine Vision

Author(s)

Chunrun Guo1, Xiaojun Liu1,2

Corresponding Author:
Xiaojun Liu
Affiliation(s)

1School of Electromechanical and Automobile Engineering, Huanggang Normal University, Huanggang 438000, Hubei, China

2Hubei Zhongke Research Institute of Industrial Technology, Huanggang 438000, Hubei, China

Abstract

In recent years, with the rapid development of electronic technology and image recognition technology, target recognition technology based on rotary wing UAV has become a hot research topic. This paper mainly studies the autonomous positioning and control system of rotary-wing UAV based on machine vision. This paper uses the weighted average method to convert the color images collected by the camera into grayscale images. The color image collected by the airborne camera is grayed out, and the binary image is obtained after threshold segmentation. The median filter technology is used to eliminate the noise, the edge information of the mark is detected, and the Harris corner points are finally extracted. After the clustering operation is completed, the traditional least squares method is used to fit a straight line, each connected component is matched, and each point is weighted according to its gradient. In this paper, the RANSAC algorithm is used to remove the mismatch points and obtain the SIFT characteristic information. At the same time, the PID control algorithm is used to obtain the deviation required for PID control. According to the rotational speed of the four motors of the deviation control system, the attitude control of the aircraft is realized. Finally, the positioning accuracy of the system is evaluated. Experimental results show that the detection time of SIFT feature points is about 100ms. The results show that machine vision improves the positioning accuracy of rotary-wing UAV and improves the accuracy of target recognition.

Keywords

Machine Vision, Rotary Wing Drone, Autonomous Positioning, Control System, Target Recognition

Cite This Paper

Chunrun Guo, Xiaojun Liu. Autonomous Positioning and Control System of Rotor UAV Based on Machine Vision. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 8: 25-38. https://doi.org/10.25236/IJFET.2021.030805.

References

[1] Sun T H , Tien F C , Tien F C , et al. Automated thermal fuse inspection using machine vision and artificial neural networks[J]. Journal of Intelligent Manufacturing, 2016, 27(3):639-651.

[2] Jiang H , Duerstock B S , Wachs J P . A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 44(5):630-641.

[3] Favret C , Sieracki J M . Machine vision automated species identification scaled towards production levels [J]. Systematic Entomology, 2016, 41(1):133-143.

[4] Li P , Zheng M , Jing J . Measurement system of garment dimension based on Machine Vision [J]. Wool Textile Journal, 2017, 45(3):42-47.

[5] Fernandez-Robles L , Azzopardi G , Alegre E , et al. Machine-vision-based identification of broken inserts in edge profile milling heads[J]. Robotics and Computer-Integrated Manufacturing, 2017, 44(4):276-283.

[6] Carroll J . Machine vision lighting companies address niche imaging requirements [J]. Vision Systems Design, 2019, 24(6):22-27.

[7] Hosseinpour S , Ilkhchi A H , Aghbashlo M . An intelligent machine vision-based smartphone app for beef quality evaluation [J]. Journal of food engineering, 2019, 248(5):9-22.

[8] Saravanan D . Machine vision technique for detection of cotton contaminations [J]. Man-Made Textiles in India, 2019, 47(12):409-413.

[9] Cubero S , Lee W S , Aleixos N , et al. Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review[J]. Food & Bioprocess Technology, 2016, 9(10):1623-1639.

[10] Xiangyang Z , Jinwu Z , Zhihong Q , et al. Straight Line Detection Algorithm in Cigarette Packet Stamp Inspection Based on Machine Vision Technology[J]. Tobacco ence & Technology, 2008, 49(7):105-109.

[11] Dawood T , Zhu Z , Zayed T . Machine vision-based model for spalling detection and quantification in subway networks [J]. Automation in Construction, 2017, 81(9):149-160.

[12] Amraei S , Abdanan Mehdizadeh S , Salari S . Broiler weight estimation based on machine vision and artificial neural network [J]. British Poultry Science, 2017, 58(2):200-205.

[13] Tang W , Tian L , Zhao X . Research on displacement measurement of disk vibration based on machine vision technique [J]. Optik - International Journal for Light and Electron Optics, 2016, 127(8):4173-4177.

[14] Naderiparizi S , Kapetanovic Z , Smith J R . Battery-Free Connected Machine Vision with WISPCam [J]. Mobile computing and communications review, 2016, 20(1):10-13.

[15] Shi L , Ren H , Wang J , et al. Latin square design for chip length machine vision measurement system analysis[J]. Quality Engineering, 2016, 28(4):381-387.

[16] You F , Li Y H , Huang L , et al. Monitoring drivers' sleepy status at night based on machine vision[J]. Multimedia Tools & Applications, 2017, 76(13):14869-14886.

[17] Xi Q , Rauschenbach T , Daoliang L . Review of Underwater Machine Vision Technology and Its Applications [J]. Marine Technology Society Journal, 2017, 51(1):75-97.

[18] Yang A , Gao X , Li M . Design of apochromatic lens with large field and high definition for machine vision [J]. Appl Opt, 2016, 55(22):5977-5985.

[19] Li-Ying C , San-Peng H E , Qian L , et al. Quantifying muskmelon fruit attributes with A-TEP-based model and machine vision measurement[J]. Journal of Integrative Agriculture, 2018, 17(006):1369-1379.

[20] Chu H H , Wang Z Y . A study on welding quality inspection system for shell-tube heat exchanger based on machine vision [J]. International Journal of Precision Engineering & Manufacturing, 2017, 18(6):825-834.

[21] Sun G , Li Y , Zhang Y , et al. Nondestructive measurement method for greenhouse cucumber parameters based on machine vision[J]. Engineering in Agriculture Environment & Food, 2016, 9(1):70-78.

[22] Hashemzadeh M , Farajzadeh N . A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry [J]. International Journal of Computational Intelligence Systems, 2016, 9(5):850-862.

[23] Jie S , Yinya L , Guoqing Q , et al. Machine vision based passive tracking algorithm with intermittent observations[J]. Journal of Huazhong University of ence and Technology (Natural ence Edition), 2017, 45(6):33-37.

[24] Tsai D M , Hsieh Y C . Machine Vision-Based Positioning and Inspection Using Expectation–Maximization Technique [J]. IEEE Transactions on Instrumentation & Measurement, 2017, 66(11):2858-2868.

[25] Kamaev A N , Sukhenko V A , Karmanov D A . Constructing and visualizing three-dimensional sea bottom models to test AUV machine vision systems [J]. Programming and Computer Software, 2017, 43(3):184-195.

[26] Liu D , Lu Z , Cao T , et al. A real-time posture monitoring method for rail vehicle bodies based on machine vision [J]. Vehicle System Dynamics, 2017, 55(6):853-874.

[27] Moore A J , Schubert M , Dolph C , et al. Machine Vision Identification of Airport Runways with Visible and Infrared Videos[J]. Journal of Aerospace Computing, Information and Communication, 2016, 13(7):1-12.

[28] Nattharith P , Guzel M S . Machine vision and fuzzy logic-based navigation control of a goal-oriented mobile robot [J]. Adaptive Behavior, 2016, 24(3):168-180.

[29] Chen J , Gu Y , Lian Y , et al. Online recognition method of impurities and broken paddy grains based on machine vision[J]. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(13):187-194.