Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-08T16:25:43.210Z Has data issue: false hasContentIssue false
  • English
  • Français

Position-Based Visual Servoing of a Mobile Robot with an Automatic Extrinsic Calibration Scheme

Published online by Cambridge University Press:  24 July 2019

Radhe Shyam Sharma Open the ORCID record for Radhe Shyam Sharma [Opens in a new window] ,
Santosh Shukla ,
Laxmidhar Behera  and
Venkatesh K. Subramanian
Radhe Shyam Sharma*
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India. E-mails: [email protected], [email protected], [email protected]
Santosh Shukla
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India. E-mails: [email protected], [email protected], [email protected]
Laxmidhar Behera
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India. E-mails: [email protected], [email protected], [email protected]
Venkatesh K. Subramanian
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, India. E-mails: [email protected], [email protected], [email protected]
*
* Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, we present and implement a novel approach for position-based visual servoing. The challenge of controlling the mobile robot while simultaneously estimating the camera to mobile robot transformation is solved. This is achieved using gradient descent (GD)-based estimation and the sliding-mode approach. The GD approach allows online parameter estimation for controlling the robot to achieve a desired position and orientation. The adaptive nature of the parameters demonstrates the robustness of the system. In contrast to existing work, the proposed technique achieves both estimation and control tasks in a single experiment. Simulation and experimental results are provided to validate the performance of the proposed scheme.

Keywords

Visual servoingGradient descentSliding-mode controlAuto extrinsic calibration
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 831 - 844
Copyright
© Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agin, G. J., Real Time Control of a Robot with a Mobile Camera (SRI International, Menlo Park, California, 1979).Google Scholar
Shirai, Y. and Inoue, H., “Guiding a robot by visual feedback in assembling tasks,” Pattern Recognit. 5(2), 99–108 (1973).CrossRefGoogle Scholar
Wang, H., Liu, Y. H., Chen, W. and Wang, Z., “A new approach to dynamic eye-in-hand visual tracking using nonlinear observers,” IEEE/ASME Trans. Mechatron. 16(2), 387–394 (2011).CrossRefGoogle Scholar
Keshmiri, M., Xie, W. F. and Mohebbi, A., “Augmented image-based visual servoing of a manipulator using acceleration command,” IEEE Trans. Ind. Electron. 61(10), 5444–5452 (2014).CrossRefGoogle Scholar
Hutchinson, S., Hager, G. D. and Corke, P. I., “A tutorial on visual servo control,” IEEE Trans. Rob. Autom. 12(5), 651–670 (1996).CrossRefGoogle Scholar
Chaumette, F. and Hutchinson, S., “Visual servo control. I. Basic approaches,” IEEE Rob. Autom. Mag. 13(4), 82–90 (2006).CrossRefGoogle Scholar
Janabi-Sharifi, F., Deng, L. and Wilson, W. J., “Comparison of basic visual servoing methods,” IEEE/ASME Trans. Mechatron. 16(5), 967–983 (2011).CrossRefGoogle Scholar
Cherubini, A., Chaumette, F. and Oriolo, G., “Visual servoing for path reaching with nonholonomic robots,” Robotica 29(7), 1037–1048 (2012).CrossRefGoogle Scholar
Chaumette, F. and Hutchinson, S., “Visual servo control. II. Advanced approaches [tutorial],” IEEE Rob. Autom. Mag. 14(1), 109–118 (2007).CrossRefGoogle Scholar
Tsai, D., Dansereau, D. G., Peynot, T. and Corke, P., “Image-based visual servoing with light field cameras,” IEEE Rob. Autom. Lett. 2(2), 912–919 (2017).CrossRefGoogle Scholar
Hao, M. and Sun, Z., “A universal state-space approach to uncalibrated model-free visual servoing,” IEEE/ASME Trans. Mechatron. 17(5), 833–846 (2012).CrossRefGoogle Scholar
Pomares, J., Perea, I. and Torres, F., “Dynamic visual servoing with chaos control for redundant robots,” IEEE/ASME Trans. Mechatron. 19(2), 423–431 (2014).CrossRefGoogle Scholar
Guenard, N., Hamel, T. and Mahony, R., “A practical visual servo control for an unmanned aerial vehicle,” IEEE Trans. Rob. 24(2), 331–340 (2008).CrossRefGoogle Scholar
Romero, H., Salazar, S. and Lozano, R., “Visual servoing applied to real-time stabilization of a multi-rotor UAV,” Robotica 30(7), 1203–1212 (2012).CrossRefGoogle Scholar
Hamel, T. and Mahony, R., “Visual servoing of an under-actuated dynamic rigid-body system: an image-based approach,” IEEE Trans. Rob. Autom. 18(2), 187–198 (2002).CrossRefGoogle Scholar
Agravante, D. J., Claudio, G., Spindler, F. and Chaumette, F., “Visual servoing in an optimization framework for the whole-body control of humanoid robots,” IEEE Rob. Autom. Lett. 2(2), 608–615 (2017).CrossRefGoogle Scholar
Fu, Y., Hsiang, T. R. and Chung, S. L., “Multi-waypoint visual homing in piecewise linear trajectory,” Robotica 31(3), 479–491 (2013).CrossRefGoogle Scholar
Aranda, M., López-Nicolás, G. and Sagüés, C., “Sinusoidal input-based visual control for nonholonomic vehicles,” Robotica 31(5), 811–823 (2013).CrossRefGoogle Scholar
Brockett, R. W., “Asymptotic Stability and Feedback Stabilization,” In: Differential Geometric Control Theory, vol. 27, no. 1 (Birkhauser, Boston, 1983) pp. 181–191.Google Scholar
Fang, Y., Dixon, W. E., Dawson, D. M. and Chawda, P., “Homography-based visual servo regulation of mobile robots,” IEEE Trans Syst. Man Cybern. Part B (Cybernetics) 35(5), 1041–1050 (2005).CrossRefGoogle Scholar
Li, B., Fang, Y. and Zhang, X., “Visual servo regulation of wheeled mobile robots with an uncalibrated onboard camera,” IEEE/ASME Trans. Mechatron. 21(5), 2330–2342 (2016).CrossRefGoogle Scholar
Fang, Y., Liu, X. and Zhang, X., “Adaptive active visual servoing of nonholonomic mobile robots,” IEEE Trans. Ind. Electron. 59(1), 486–497 (2012).CrossRefGoogle Scholar
Becerra, H. M., López-Nicolás, G. and Sagüés, C., “A sliding-mode-control law for mobile robots based on epipolar visual servoing from three views,” IEEE Trans. Rob. 27(1), 175–183 (2011).CrossRefGoogle Scholar
Zhang, X., Fang, Y., Li, B. and Wang, J., “Visual servoing of nonholonomic mobile robots with uncalibrated camera-to-robot parameters,” IEEE Trans. Ind. Electron. 64(1), 390–400 (2017).CrossRefGoogle Scholar
Almeida, J. S., Marinho, L. B. and Souza, J. W. M., “Localization system for autonomous mobile robots using machine learning methods and omnidirectional sonar,” IEEE Lat. Am. Trans. 16(2), 368–374 (2018).CrossRefGoogle Scholar
Marinho, L. B., Rebouas Filho, P. P., Almeida, J. S., Souza, J. W. M., Junior, A. H. S. and C. de Albuquerque, V. H., “A novel mobile robot localization approach based on classification with rejection option using computer vision,” Comput. Electr. Eng. 68, 26–43 (2018).CrossRefGoogle Scholar
Marinho, L. B., Almeida, J. S., Souza, J. W. M., Albuquerque, V. H. C. and Rebouas Filho, P. P., “A novel mobile robot localization approach based on topological maps using classification with reject option in omnidirectional images,” Expert Syst. Appl. 72, 1–17 (2017).CrossRefGoogle Scholar
Sampaio, F., Silva, E. T., da Silva, L. C. and Rebouas Filho, P. P., “An Embedded Classifier for Mobile Robot Localization Using Support Vector Machines and Gray-Level Co-occurrence Matrix,” International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland (2018) pp. 201–213.Google Scholar
P. da Silva, S. P., Marinho, L. B., Almeida, J. S. and Rebouas Filho, P. P., “A Novel Approach forMobile Robot Localization in Topological Maps Using Classification with Reject Option from Structural Co-occurrence Matrix,” International Conference on Computer Analysis of Images and Patterns, Ystad, Sweden (2017) pp. 3–15.Google Scholar
P. da Silva, S. P., M. da Nbrega, R. V., Medeiros, A. G., Marinho, L. B., Almeida, J. S. and Reboucas Filho, P. P., “Localization of Mobile Robots with Topological Maps and Classification with Reject Option using Convolutional Neural Networks in Omnidirectional Images,” International Joint Conference on Neural Networks, Rio, Brazil (2018) pp. 1–8.Google Scholar
Zhang, X., Fang, Y. and Liu, X., “Motion-estimation-based visual servoing of nonholonomic mobile robots,” IEEE Trans. Rob. 27(6), 1167–1175 (2011).CrossRefGoogle Scholar
Garrido-Jurado, S., Muoz-Salinas, R., Madrid-Cuevas, F. J. and Marn-Jimnez, M. J., “Automatic generation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognit. 47(6), 2280–2292 (2014).CrossRefGoogle Scholar
Li, B., Zhang, X., Fang, Y. and Shi, W., “Visual servo regulation of wheeled mobile robots with simultaneous depth identification,” IEEE Trans. Ind. Electron. 65(1), 460–469 (2018).CrossRefGoogle Scholar
“Position based visual servoing with online parameter estimation.” https://youtu.be/IlIY5lynRgU.Google Scholar