Telerobotics, automation, and human supervisory control
Telerobotics, automation, and human supervisory control
Manual control with delays: a bibliography
ACM SIGGRAPH Computer Graphics - Special issue on interactive entertainment design, implementation and adrenaline
Scanning physical interaction behavior of 3D objects
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 1.2
OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 1.2
Real-Time Elastic Deformations of Soft Tissues for Surgery Simulation
IEEE Transactions on Visualization and Computer Graphics
A Delay Model for Router Microarchitectures
IEEE Micro
Modeling the Dynamics of the Human Thigh for a Realistic Echographic Simulator with Force Feedback
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Smith Predictor Type Control Architectures for Time Delayed Teleoperation
International Journal of Robotics Research
Real-time adaptive control for haptic telemanipulation with Kalman active observers
IEEE Transactions on Robotics
Remote minimally invasive surgery --haptic feedback and selective automation in medical robotics
Applied Bionics and Biomechanics - Surgical Robotics
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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.