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Uncalibrated Vision-Based Control and Motion Planning of Robotic Arms in Unstructured Environments Open Access


Other title
Trifocal Tensor
Motion Planning
Visual Servoing
Vision-Based Control
Robust Statistics
Three-View Geometry
Type of item
Degree grantor
University of Alberta
Author or creator
Shademan, Azad
Supervisor and department
Jagersand, Martin (Computing Science)
Examining committee member and department
Hutchinson, Seth (Electrical and Computer Engineering, University of Illinois at Urbana-Champaign)
Greiner, Russell (Computing Science)
Tavakoli, Mahdi (Electrical and Computer Engineering)
Szepesvari, Csaba (Computing Science)
Zhang, Hong (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Many robotic systems are required to operate in unstructured environments. This imposes significant challenges on algorithm design. Particularly, motion control and planning algorithms should be robust to noise and outliers, because uncertainties are inevitable. In addition, independence from scene model and calibration parameters is preferred; otherwise, the tedious model extraction and calibration procedures need to be redone with every change in the environment. The basic problem that this dissertation addresses is how to robustly control the motion of a vision-based manipulator and plan occlusion-free paths in unstructured environments. Vision-based motion control without using calibration or a geometric model is studied in Uncalibrated Visual Servoing (UVS). In this dissertation, we adopt a framework based on UVS and contribute to two distinct areas: robust visual servoing and robust randomized path planning. We develop a statistically robust algorithm for UVS, which detects outliers and finds robust estimates of the uncalibrated visual-motor Jacobian, a central matrix in the visual servoing control law. We integrate the robust Jacobian estimation into a real-time feedback control loop and present case studies. To avoid the visual and joint-limit constraints, we propose a robust sampling-based path planning algorithm. The proposed planner fits well within the UVS framework and facilitates occlusion-free paths, despite not knowing the obstacle model. Finally, our third and last contribution is a novel UVS approach based on extracting the geometry of three images in the form of the trifocal tensor. We experimentally validate this approach and show that the proposed UVS controller handles some of the most challenging degenerate configurations of image-based visual servoing.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
A. Shademan, A.-M. Farahmand, and M. Jagersand, “Robust jacobian estimation for uncalibrated visual servoing,” in Proc. IEEE Int. Conf. Robot. Automat., 2010, pp. 5564–5569.A. Shademan and M. Jagersand, “Robust sampling-based planning for uncalibrated visual servoing,” to appear in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2012. [Accepted].A. Shademan and M. Jagersand, "Three-view uncalibrated visual servoing,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2010, pp. 6234–6239.A. Shademan, A.-M. Farahmand, M. Jagersand, "Robust Uncalibrated Visual Servoing for Autonomous On-Orbit Servicing," Proc. of International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), Sapporo, Japan, Aug. 29 - Sep. 1, 2010.

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File title: Uncalibrated Vision-Based Control and Motion Planning of Robotic Arms in Unstructured Environments
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