Telepresence across delayed networks: a combined prediction and compression approach

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 utilised motion prediction scheme was shown to improve operator performance, but had no influence on operator immersion. The force prediction decreased the deviation between the delayed and the expected forces, thereby stabilising the control loop. The developed haptic data compression scheme reduced the number of packets sent across the network by 86%