Abstract
Manipulation has been a rising focus of robotics research, from industrial automation to personal service robots. Most existing control method are based on accurate kinematic modelling of robot manipulators and robot calibration that needs trained personnel, laboratory environment and data collection is time-consuming. However, robots, especially service robots, similar to other mechanical devices can be affected by slight changes or drifts caused by wear of parts, dimensional drifts, and tolerances, and all of them need a new calibration. Most methods lack robustness due to these slight changes. In this work, for mobile robots, we propose a convenient robot-camera calibration approach and a flexible control approach based on simple robot sensor calibration and pose repeatability of arm. In prototype grasping experiments on the robot Kejia, the approach is validated to be effective and robust, achieving a high success rate of grasping, and using the same control policy, we achieve 1st place in Manipulation and Object Recognition of RoboCup@Home League 2016, here Manipulation and Object Recognition is a sub challenge of Home League.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tsai, R.Y., Lenz, R.K.: A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. IEEE Trans. Robot. Autom. 5(3), 345–358 (1989)
Shiu, Y.C., Ahmad, S.: Calibration of wrist-mounted robotic sensors by solving homogeneous transform equations of the form AX = XB. IEEE Trans. Robot. Autom. 5(1), 16–29 (1989)
Wang, H., Lu, X., Hu, Z., Li, Y.: A vision-based fully-automatic calibration method for hand-eye serial robot. Ind. Robot Int. J. 42(1), 64–73 (2015)
Daniilidis, K.: Handeye calibration using dual quaternions. Int. J. Robot. Res. 18(3), 286–298 (1999)
Dornaika, F., Horaud, R.: Simultaneous robot-world and hand-eye calibration. IEEE Trans. Robot. Autom. 14(4), 617–622 (1998)
Heller, J., Havlena, M., Pajdla, T.: A branch-and-bound algorithm for globally optimal hand-eye calibration. In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, pp. 1608–1615 (2012)
Horn, B.: Closed-form solution of absolute orientation using unit quatemions. JOSA A 4(4), 629–642 (1987)
Schonemann, P.: A generalized solution of the orthogonal Procrustes problem. Psychometrika 31, 1–10 (1966)
Voruganti, A.K.R., Bartz, D.: Alternative online extrinsic calibration techniques for minimally invasive surgery. In: Proceedings of the 2008 ACM Symposium on Virtual Reality Software and Technology, VRST 2008, pp. 291–292. ACM (2008)
Chen, E.C.: An augmented reality platform for planning of minimally invasive cardiac surgeries. Proc. SPIE Med. Imaging Int. Soc. Opt. Photon. 8316, 831617 (2012)
Levine, S., Pastor, P., Krizhevsky, A., Quillen D.: Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. In: International Symposium on Experimental Robotics (ISER) (2016)
De la Escalera, A., Armingol, J.M.: Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration. Sensors 10(3), 2027–2044 (2010)
Halir, R., Flusser, J.: Numerically stable direct least squares fitting of ellipses. In: Proceedings of International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 125–132 (1998)
Shah, M., Eastman, R.D., Hong, T.: An overview of robot-sensor calibration methods for evaluation of perception systems. In: Workshop on Performance Metrics for Intelligent Systems, pp. 15–20 (2012)
Roth, Z., Mooring, B., Ravani, B.: An overview of robot calibration. IEEE J. Robot. Autom. 3(5), 377–385 (1987)
Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation, Shanghai, pp. 1–4 (2011)
Fiala, M.: ARTag, a fiducial marker system using digital techniques. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 590–596 (2005)
Wasenmüller, O., Stricker, D.: Comparison of kinect V1 and V2 depth images in terms of accuracy and precision. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016. LNCS, vol. 10117, pp. 34–45. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54427-4_3
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 61573333), the Joint Funds of the National Natural Science Foundation of China (Grand No. U1613216) and the Fundamental Research Funds for the Central Universities (Grant No. 3022016WK6030000078).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Cui, G., Chen, G., Zhang, Z., Chen, X. (2018). A Flexible Grasping Policy Based on Simple Robot-Camera Calibration and Pose Repeatability of Arm. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10985. Springer, Cham. https://doi.org/10.1007/978-3-319-97589-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-97589-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97588-7
Online ISBN: 978-3-319-97589-4
eBook Packages: Computer ScienceComputer Science (R0)