Drip-Tape-Following Approach Based on Machine Vision for a Two-Wheeled Robot Trailer in Strip Farming

Chung-Liang Chang, Hung-Wen Chen, Yung-Hsiang Chen and Chang-Chen Yu
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Chung-Liang Chang: Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Neipu 91201, Pingtung, Taiwan
Hung-Wen Chen: Department of Biomechatronics Engineering, National Pingtung University of Science and Technology, Neipu 91201, Pingtung, Taiwan
Yung-Hsiang Chen: Department of Vehicle Engineering, National Pingtung University of Science and Technology, Neipu 91201, Pingtung, Taiwan
Chang-Chen Yu: Department of Vehicle Engineering, National Pingtung University of Science and Technology, Neipu 91201, Pingtung, Taiwan

Agriculture, 2022, vol. 12, issue 3, 1-18

Abstract: Due to the complex environment in the field, using machine vision technology to enable the robot to travel autonomously was a challenging task. This study investigates a method based on mathematical morphology and Hough transformation for drip tape following by a two-wheeled robot trailer. First, an image processing technique was utilized to extract the drip tape in the image, including the selection of the region of interest (ROI), Red-Green-Blue (RGB) to Hue-Saturation-Value (HSV) color space conversion, color channel selection, Otsu’s binarization, and morphological operations. The line segments were obtained from the extracted drip tapes image by a Hough line transform operation. Next, the deviation angle between the line segment and the vertical line in the center of the image was estimated through the two-dimensional law of cosines. The steering control system could adjust the rotation speed of the left and right wheels of the robot to reduce the deviation angle, so that the robot could stably travel along the drip tape, including turning. The guiding performance was evaluated on the test path formed by a drip tape in the field. The experimental results show that the proposed method could achieve an average line detection rate of 97.3% and an average lateral error of 2.6 ± 1.1 cm, which was superior to other drip-tape-following methods combined with edge detection, such as Canny and Laplacian.

Keywords: machine vision; image processing; two-wheeled robot trailer; steering control; strip farming (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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