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Simultaneously learning to recognize and control a low-cost robotic arm
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
2009 (English)In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 27, no 11, 1729-1739 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a visual servoing method based on a learned mapping between feature space and control space. Using a suitable recognition algorithm, we present and evaluate a complete method that simultaneously learns the appearance and control of a low-cost robotic arm. The recognition part is trained using an action precedes perception approach. The novelty of this paper, apart from the visual servoing method per se, is the combination of visual servoing with gripper recognition. We show that we can achieve high precision positioning without knowing in advance what the robotic arm looks like or how it is controlled.

Place, publisher, year, edition, pages
2009. Vol. 27, no 11, 1729-1739 p.
Keyword [en]
Gripper recognition; Jacobian estimation; LWPR; Visual servoing
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-21195DOI: 10.1016/j.imavis.2009.04.003OAI: oai:DiVA.org:liu-21195DiVA: diva2:240889
Note
Original Publication: Fredrik Larsson, Erik Jonsson and Michael Felsberg, Simultaneously learning to recognize and control a low-cost robotic arm, 2009, Image and Vision Computing, (27), 11, 1729-1739. http://dx.doi.org/10.1016/j.imavis.2009.04.003 Copyright: Elsevier Science B.V., Amsterdam. http://www.elsevier.com/ Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2017-12-13Bibliographically approved
In thesis
1. Methods for Visually Guided Robotic Systems: Matching, Tracking and Servoing
Open this publication in new window or tab >>Methods for Visually Guided Robotic Systems: Matching, Tracking and Servoing
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with three topics; Bayesian tracking, shape matching and visual servoing. These topics are bound together by the goal of visual control of robotic systems. The work leading to this thesis was conducted within two European projects, COSPAL and DIPLECS, both with the stated goal of developing artificial cognitive systems. Thus, the ultimate goal of my research is to contribute to the development of artificial cognitive systems.

The contribution to the field of Bayesian tracking is in the form of a framework called Channel Based Tracking (CBT). CBT has been proven to perform competitively with particle filter based approaches but with the added advantage of not having to specify the observation or system models. CBT uses channel representation and correspondence free learning in order to acquire the observation and system models from unordered sets of observations and states. We demonstrate how this has been used for tracking cars in the presence of clutter and noise.

The shape matching part of this thesis presents a new way to match Fourier Descriptors (FDs). We show that it is possible to take rotation and index shift into account while matching FDs without explicitly de-rotate the contours or neglecting the phase. We also propose to use FDs for matching locally extracted shapes in contrast to the traditional way of using FDs to match the global outline of an object. We have in this context evaluated our matching scheme against the popular Affine Invariant FDs and shown that our method is clearly superior.

In the visual servoing part we present a visual servoing method that is based on an action precedes perception approach. By applying random action with a system, e.g. a robotic arm, it is possible to learn a mapping between action space and percept space. In experiments we show that it is possible to achieve high precision positioning of a robotic arm without knowing beforehand how the robotic arm looks like or how it is controlled.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 93 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1416
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-51657 (URN)LIU-TEK-LIC-2009:24 (Local ID)978-91-7393-527-2 (ISBN)LIU-TEK-LIC-2009:24 (Archive number)LIU-TEK-LIC-2009:24 (OAI)
Presentation
2009-12-18, Glashuset, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2009-11-25 Created: 2009-11-12 Last updated: 2016-05-04Bibliographically approved
2. Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications
Open this publication in new window or tab >>Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traffic accidents are globally the number one cause of death for people 15-29 years old and is among the top three causes for all age groups 5-44 years. Much of the work within this thesis has been carried out in projects aiming for (cognitive) driver assistance systems and hopefully represents a step towards improving traffic safety.

The main contributions are within the area of Computer Vision, and more specifically, within the areas of shape matching, Bayesian tracking, and visual servoing with the main focus being on shape matching and applications thereof. The different methods have been demonstrated in traffic safety applications, such as  bicycle tracking, car tracking, and traffic sign recognition, as well as for pose estimation and robot control.

One of the core contributions is a new method for recognizing closed contours, based on complex correlation of Fourier descriptors. It is shown that keeping the phase of Fourier descriptors is important. Neglecting the phase can result in perfect matches between intrinsically different shapes. Another benefit of keeping the phase is that rotation covariant or invariant matching is achieved in the same way. The only difference is to either consider the magnitude, for rotation invariant matching, or just the real value, for rotation covariant matching, of the complex valued correlation.

The shape matching method has further been used in combination with an implicit star-shaped object model for traffic sign recognition. The presented method works fully automatically on query images with no need for regions-of-interests. It is shown that the presented method performs well for traffic signs that contain multiple distinct contours, while some improvement still is needed for signs defined by a single contour. The presented methodology is general enough to be used for arbitrary objects, as long as they can be defined by a number of regions.

Another contribution has been the extension of a framework for learning based Bayesian tracking called channel based tracking. Compared to earlier work, the multi-dimensional case has been reformulated in a sound probabilistic way and the learning algorithm itself has been extended. The framework is evaluated in car tracking scenarios and is shown to give competitive tracking performance, compared to standard approaches, but with the advantage of being fully learnable.

The last contribution has been in the field of (cognitive) robot control. The presented method achieves sufficient accuracy for simple assembly tasks by combining autonomous recognition with visual servoing, based on a learned mapping between percepts and actions. The method demonstrates that limitations of inexpensive hardware, such as web cameras and low-cost robotic arms, can be overcome using powerful algorithms.

All in all, the methods developed and presented in this thesis can all be used for different components in a system guided by visual information, and hopefully represents a step towards improving traffic safety.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 49 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1395
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-71664 (URN)978-91-7393-074-1 (ISBN)
Public defence
2011-11-25, Vallfarten, hus Vallfarten, Campus Valla, Linköpings universitet, Linköping, 09:15 (English)
Opponent
Supervisors
Available from: 2011-10-28 Created: 2011-10-28 Last updated: 2016-05-04Bibliographically approved

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Larsson, FredrikJonsson, ErikFelsberg, Michael

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