Abstract
A simple and inexpensive system but effective in performing required tasks is the most preferable in industry. In this study, a vision system is developed to solve the peg-in-hole problem of a robot-like forklift to pick up copper wire spool arranged side by side on a rack, without using any sensors, except a low-cost camera. Inspired by how human perceive an object orientation based on its shape, an algorithm is developed to determine robot orientation based on the shape of a copper wire spool relative to camera position and yaw angle. The center point of the spool (CPS) should be on the center line of camera FOV (CFOV) if the camera is perpendicular or 0° parallel to the spool. Thus, the coordinate of the CPS and the CFOV is same. Instead, when the camera is seeing the spool from the angle less or bigger than 0°, the CPS and CFOV will be different, and the difference shows the position and the yaw angle of the camera relative to the spool. A copper wire spool has three circles; the outer circle, the tapper part around its center hole and the center hole itself. The proposed system uses Circular Hough Transform (CHT), filtering, binary, morphology and Sobel edge detection of the sampled images from real-time video recording to determine the orientation of the camera related to the copper wire spool shape, in which the center coordinate of the three circles was determined. Results from the experiments that had been done show that the system is able to determine the orientation of the camera related to the spool.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Suzuki Y, Koyama K, Ming A, Shimojo M (2015) Grasping strategy for moving object using net-structure proximity sensor and vision sensor. In: 2015 IEEE international conference on robotics and automation (ICRA), pp 1403–1409
Park H, Park J, Lee DH, Park JH, Baeg MH, Bae JH (2017) Compliance-based robotic peg-in-hole assembly strategy without force feedback. IEEE Trans Ind Electron 64:6299–6309
Polverini MP, Zanchettin AM, Castello S, Rocco P (2016) Sensorless and constraint based peg-in-hole task execution with a dual-arm robot. In: Proceedings—IEEE international conference on robotics and automation, pp 415–420
Lin LL, Yang Y, Song YT, Nemec B, Ude A, Rytz JA, Buch AG, Kruger N, Savarimuthu TR (2015) Peg-in-hole assembly under uncertain pose estimation. In: Proceedings world congress on intelligent control and automation, pp 2842–2847
Chang WC, Wu CH (2017) Automated USB peg-in-hole assembly employing visual serving. In: 2017 3rd international conference on automation, control and robots, ICCAR 2017, pp 352–355
Yadav VK, Batham S, Acharya AK, Paul R (2014) Approach to accurate circle detection: circular hough transform and local maxima concept. In: 2014 international conference on electronics and communication systems, ICECS 2014, pp 3–7
Lo R-C, Hsu H-C (2016) A circular band extraction method based on extended hough transform. Int J Pattern Recognit Artif Intell 30:1655021
Djekoune AO, Messaoudi K, Amara K (2017) Incremental circle hough transform: an improved method for circle detection. Opt Int J Light Electron Opt 133: 17–31
Yao Z, Yi W (2016) Curvature aided Hough transform for circle detection. Expert Syst Appl 51:26–33
Ogawa K, Ito Y, Nakano K (2010) Efficient Canny edge detection using a GPU. In: 2010 first international conference on communication, networks and computings, pp 279–280
Shrivakshan GT, Chandrasekar C (2012) A comparison of various edge detection techniques used in image processing. Int J Comput Sci Issues 9:269–276
De Natale FGB, Boato G (2017) Detecting morphological filtering of binary images. IEEE Trans Inf Forensics Secur 12:1207–1217
Othman Z, Rafiq M, Kadir A (2009) Comparison of Canny and Sobel edge detection in MRI images. Comput Sci Biomech Tissue Eng Group Inf Syst 133–136
Hussin R, Juhari MR, Kang NW, Ismail RC, Kamarudin A (2012) Digital image processing techniques for object detection from complex background image. Proc Eng 41:340–344
Tsarouchi P, Matthaiakis SA, Michalos G, Makris S, Chryssolouris G (2016) A method for detection of randomly placed objects for robotic handling. CIRP J Manuf Sci Technol 14:20–27
Meng Y, Zhang Z, Yin H, Ma T (2018) Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. Micron 106:34–41
Tooei MHDH, Mianroodi JR, Norouzi N, Khajooeizadeh A (2011) An innovative implementation of circular Hough transform using eigenvalues of covariance matrix for detecting circles. In: Proceedings ELMAR-2011, pp 397–400
Lestriandoko NH, Sadikin R (2017) Circle detection based on hough transform and Mexican Hat filter. In: Proceeding—2016 international conference on computer, control, informatics and its application. Recent Prog. Comput. Control. Informatics Data Sci. IC3INA 2016, pp 153–157
Li D, Nan F, Xue T, Yu X (2017) Circle detection of short arc based on randomized Hough transform. In: 2017 IEEE international conference on mechatronics and automation (ICMA), pp 258–263
Acknowledgements
This research is supported by Universiti Malaysia Pahang Internal Grant of RDU1703143. The authors would also like to thank the Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang for providing the facilities to conduct this research and financial support throughout the process.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Azman, F.A., Daud, M.R., Mohamed, A.I., Irawan, A., Ismail, R.M.T.R., Saari, M.M. (2020). Camera Orientation Determination Based on Copper Wire Spool Shape. In: Kasruddin Nasir, A.N., et al. InECCE2019. Lecture Notes in Electrical Engineering, vol 632. Springer, Singapore. https://doi.org/10.1007/978-981-15-2317-5_18
Download citation
DOI: https://doi.org/10.1007/978-981-15-2317-5_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2316-8
Online ISBN: 978-981-15-2317-5
eBook Packages: EngineeringEngineering (R0)