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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]
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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

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