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.
This paper presents two methods aimed at alleviating the negative effects of network delays on teleoperation. The problem of telepresence across delayed networks is well known. A delay in feedback information such as visual and haptic data can make the task at hand very unintuitive and difficult for the operator. The first presented method investigates the hypothesis that simulated inertia in the haptic input device can be a supporting factor during teleoperation across delayed networks. An experiment involving 36 human subjects was carried out under varying network and inertia conditions. Psychophysical experiments were conducted to determine suitable values of inertia. However, simulated inertia was found to be neither a supporting factor nor a detrimental factor to operator performance and immersion in the presence of both delayed and non-delayed networks. The second presented method is a force prediction approach, which extends the teleoperation system with a local force model. This is a learned force model situated locally at the operator-side. Instead of relying on the delayed force signals from the teleoperator-side, haptic information can be extracted from this local force model. An experiment has been created to demonstrate the benefits of this approach in compensating for the instabilities due to time delay. IntroductionTelepresence technologies provide promising solutions to applications such as long distance surgery, remote steering of explorer vehicles, operations in dangerous nuclear environments, and manual microassembly of small production lots. Typical telepresence scenarios contain three main components:(1) a human operator, (2) a teleoperator to complete a desired operation, and (3) a communication platform between the two. The human operator is provided with all the necessary input devices and feedback information, such as graphics and forces, to complete the operation at hand. On the other side, the teleoperator is equipped with the necessary sensors (e.g., force and contact sensors) and equipment (e.g., video cameras) to deliver the necessary feedback information to the operator.Within the communication layer, the presence of large delays (e.g., Ͼ100 ms) can significantly reduce operator intuition (Jeffay, Hudson, & Parris,
Telesurgery systems integrate multimodal communication and robotic technologies to enable surgical procedures to be performed from remote locations. They allow human surgeons to intuitively control laparoscopic instruments and to navigate within the human body. In this paper, we present selected topics on multimodal interaction in the context of telesurgery applications. These are results from the collaborative research project SFB 453 on "High-Fidelity Telepresence and Teleaction" which is funded by the German Research Foundation in the larger Munich area. The focus in this paper is on multimodal information processing and communication including simulation of surgical targets in the human body. Furthermore, we present an overview of our advanced multimodal telesurgery demonstrators that provide a comprehensive platform for our collaborative telepresence research.
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