Flexible Adjustment of Endpoint Stiffness of a Robotic Arm
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2017-02-01
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en
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Abstract
Robots, and therefore robotic arms, are becoming more popular in everyday
life. In order to be useful and safe these robots need to be
exible and
able to adjust to their environment. Franklin et al. (2008) proposed an
algorithm that regulates the sti ness of the human arm, which makes it easier
to control in unstable and uncertain environments. The goal of this project
was to implement a similar learning algorithm onto a 2-link robotic arm.
Simulations were done, in order to obtain information about how di erent
retention ( ) and learning ( ) factors would in
uence the sti ness and what
combinations of these factors would lead to a reasonable increase and decrease
of sti ness. These simulations showed that there was no perfect set of and
, but did gave an indication of what sets would be able to reach a reasonable
sti ness. In the experiment it was found that the robotic arm was able to
adjust its sti ness for the left-right and fore-aft movements made by the
human. However many improvements upon this algorithm can be made,
such that the increase and decrease of sti ness ts even better to variable
situations.
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Faculteit der Sociale Wetenschappen