Abstract
Vision sensors are used to estimate the pose (position and orientation) of mating components in a vision assisted robotic peg-in-hole assembly which is a crucial step in aligning the mating hole-component with the corresponding moving peg-component. The accuracy of this estimation decides the performance of peg-in-hole robotic assembly with an appropriate mapping between the image and task environment using a fixed overhead camera or camera on robot arm. The wheel and hub assembly in automobile has multiple holes and pegs in their mating parts which lead to more complex pose estimation procedure. The success rate of the assembly process (without jamming) is affected by an inaccurate pose estimation which leads to lateral and/or axial misalignment between the mating components during its insertion phase. On this consideration, this work proposes a pose estimation algorithm for a multiple peg-in-hole assembly with the use of genetic algorithm based two-stage camera calibration procedure. The proposed algorithm has also been tested for its performance in estimating the pose of the multiple pegs in wheel-hub of a car. The result reveals that the proposed method estimates the pose of the pegs accurately with minimum re-projection error.
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References
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32
Chaumette, F., Hutchinson, S.: Visual servo control part I: basic approaches. IEEE Robot. Autom. Mag. 13, 82–90 (2006)
Chaumette, F.: Potential problems of stability and convergence in image based and position based visual servoing. In: Kriegman, D.J., Hager, G.D., Morse, A.S. (eds.) The Confluence of Vision and Control. LNCIS, vol. 237, pp. 66–78. Springer, London (1998). doi:10.1007/BFb0109663
Dong, G., Zhu, Z.: Position based visual servo control of autonomous robotic manipulators. Acta Astronaut. 115, 291–302 (2015)
Ji, Q., Zhang, Y.: Camera calibration with genetic algorithms. IEEE Trans. Syst. Man. Cybern. Part A: Syst. Hum. 31, 120–130 (2001)
Lee, S., Kim, E., Park, Y.: 3D object recognition using multiple features for robotic manipulation. In: Proceedings of IEEE International Conference on Robotics and Automation, USA, pp. 3768–3774 (2006)
Lenz, R., Tsai, R.: Techniques for calibration of the scale factor and image center for high accuracy 3D machine vision metrology. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 68–75 (1987)
Li, W., Gee, T., Friedrich, H., Delmas, P.: A practical comparison between Zhang’s and Tsai’s calibration approaches. In: Proceedings of International Conference on Image and Vision Computing (IVCNZ), New Zealand, pp. 166–171, November 2014
Mallis, E.: Survey of vision based robot control. In: Proceedings of European Naval Ship Design, Captain Computer IV Forum, Brest, France, pp. 1–16 (2002)
Marchand, E., Boutherny, P., Chaumette, F., Moreau, V.: Robust real time visual tracking using a 2D-3D model based approach. In: Proceedings of the 1999 IEEE International Conference on Computer Vision, pp. 262–268 (1999)
Mathworks Circle Detection Algorithm, June 2016. http://in.mathworks.com/help/images/examples/detect-and-measure-circular-objects-in-an-image.htmlaccessed
Nagarajan, P., Saravana Perumaal, S.: GA based camera calibration for vision assisted robotic assembly system. IET Comput. Vision (2016). doi:10.1049/iet-cvi.2016.0004
Song, K.T., Chang, C.H.: Object pose estimation for grasping based on robust center point detection. In: proceedings of 8th Asian Control Conference, Taiwan , pp. 305–310 (2011)
Song, X., Yang, B., Feng, Z., et al.: Camera calibration based on particle swarm optimization. In: Proceedings of IEEE International Conference on Image and Signal Processing, Tianjin, pp. 1–5 (2009)
Tsai, R.: A versatile camera calibration technique for high accuracy 3D machine vision metrology using off the shelf TV cameras and Lenses. IEEE J. Robot. Autom. 3(4), 323–344 (1987)
Weng, J., Cohen, P., Herniou, M.: Camera calibration with distortion models and accuracy evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992)
Yang, Z., Chen, F., Zhao, J., et al.: A novel camera calibration method based on genetic algorithm. IEEE Conference on Industrial Electronics and Applications, Singapore, pp. 2222–2227, June 2008
Youngrock, Y., DeSouza, G.N., Avinash Kak, C.: Real-time tracking and pose estimation for Industrial objects using geometric features. In: Proceedings of IEEE International Conference on Robotics and Automation, Taiwan, pp. 3473–3478 (2003)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000)
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Nagarajan, P., Saravana Perumaal, S., Yogameena, B. (2017). Vision Based Pose Estimation of Multiple Peg-in-Hole for Robotic Assembly. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_5
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