Prediction-based methods for teleoperation across delayed networks |
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Authors: | Stella Clarke Gerhard Schillhuber Michael F Zaeh Heinz Ulbrich |
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Affiliation: | 1.Institute for Machine Tools and Industrial Management,Technische Universit?t München,Garching,Germany;2.Institute of Applied Mechanics,Technische Universit?t München,Garching,Germany |
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Abstract: | The remote nature of telepresence scenarios can be seen as a strongpoint and also as a weakness. Although it enables the remote
control of robots in dangerous or inaccessible environments, it necessarily involves some kind of communication mechanism
for the transmission of control signals. This communication mechanism necessarily involves adverse network effects such as
delay. Three mechanisms aimed at improving the effects of network delay are presented in this paper: (1) Motion prediction
to partially compensate for network delays, (2) force prediction to learn a local force model, thereby reducing dependency
on delayed force signals, and (3) haptic data compression to reduce the required bandwidth of high frequency data. The utilized
motion prediction scheme was shown to improve operator performance, but had no influence on operator immersion. The force
prediction provided haptic feedback through synchronous forces from the local model, thereby stabilizing the control loop.
The developed haptic data compression scheme reduced the number of packets sent across the network by 90%, while improving
the quality of the haptic feedback. |
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