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