A Trust-Region-based Robot Controller
for Image-based Visual Servoing
in Six Degrees of Freedom
MSC 2000: 93C85 93C57 70E60 93C40.
Diplom thesis in Mathematics, carried out at the Centre of Technical Mathematics
and the Institute of Automation
University of Bremen, Germany
Nils T Siebel
(A Diplom is a 5--6 year study course with a 100-page thesis)
Abstract
In this project an image-based visual servoing robot controller was
designed, implemented and validated.
In order to be able to manipulate an object the controller's task is to
move the robot end-effector into a desired relative position and
orientation to the object.
The location of the object in robot space is unknown.
Only image data from a CCD camera attached to the robot's end-effector
(eye in hand) is used to calculate robot movements as controller
output.
Image features corresponding to the desired pose were determined by moving
the robot there and acquiring an image (teaching by showing).
The object carries a label with 4 markings.
The difference between their actual and desired positions in the image
(image error) is the only controller input.
The implemented algorithm is of the type image-based static
look and move.
Because of their robustness towards model errors, applications for
image-based robot controllers include autonomous robot systems.
An example is the Institute of Automation's
FRIEND system to
support disabled people where the controller is currently ported to.
A serious problem of previously implemented controllers is the handling
of errors in the system model, particularly external camera parameters.
Especially with an eye in hand camera, resulting large inaccurate robot
movements can produce undesired effects.
Apart from the danger of hitting objects or people inside the robot
workspace object markings are often no longer visible in the image and the
controller does not converge (see chapter 3 of the thesis).
Using a trust-region method to determine its output, the newly
developed visual servoing controller prevents these problems.
This is done by measuring model errors and automatically adapting a
maximum step length for the controller.
By taking as long steps as possible while maintaining convergence the new
controller guarantees a successful visual servoing process.
At the same time the number of steps required to move the robot into the
desired pose is kept very small.
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Last Modified on Wed Feb 25 2004.
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In this project an image-based visual servoing robot controller was
designed, implemented and validated.
In order to be able to manipulate an object the controller's task is to
move the robot end-effector into a desired relative position and
orientation to the object.
The location of the object in robot space is unknown.
Only image data from a CCD camera attached to the robot's end-effector
(eye in hand) is used to calculate robot movements as controller
output.
Image features corresponding to the desired pose were determined by moving
the robot there and acquiring an image (teaching by showing).
The object carries a label with 4 markings.
The difference between their actual and desired positions in the image
(image error) is the only controller input.
The implemented algorithm is of the type image-based static
look and move.
Because of their robustness towards model errors, applications for
image-based robot controllers include autonomous robot systems.
An example is the Institute of Automation's
FRIEND system to
support disabled people where the controller is currently ported to.