Force feedback time prediction based on neural network of MIS Robot with time delay

Robotic technology is enhancing surgery through improved precision, stability, and dexterity. In manual MIS, the surgeon is separated from the operation area, which is reached by long instruments. In image-guided procedures, image prediction technique based on visual reality technology has solved time-delay problem of image information between master and slave manipulator of teleoperation system effectively in many application fields. However, time-delay of force feedback information which is transmitted from communication link is also inconvenient to the operator's working and makes a bad influences on the system's stability and transparency. In this paper, the start and stop time of feedback force torque can be predicted by using RBF neural network technology when the slave manipulator is interacting with the environment, such that the force feedback information can synchronize with the predictive image. Simulation results show excellence of the proposed scheme.

[1]  G. L. Ricard,et al.  Manual control with delays: a bibliography , 1994, COMG.

[2]  Herve Delingette,et al.  Real-Time Elastic Deformations of Soft Tissues for Surgery Simulation , 1999, IEEE Trans. Vis. Comput. Graph..

[3]  Mark W. Spong,et al.  Bilateral control of teleoperators with time delay , 1988, Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics.

[4]  Günther Schmidt,et al.  Force-reflecting Telepresence in Extensive Remote Environments , 2007, J. Intell. Robotic Syst..

[5]  Keyvan Hashtrudi-Zaad,et al.  Smith Predictor Type Control Architectures for Time Delayed Teleoperation , 2006, Int. J. Robotics Res..

[6]  P. Hinterseer,et al.  Model based data compression for 3D virtual haptic teleinteraction , 2006, 2006 Digest of Technical Papers International Conference on Consumer Electronics.

[7]  Linda Elizabeth Mar Human control performance in operation of a time-delayed master-slave telemanipulator , 1985 .

[8]  Fei Shu The Research and Development of Force Reflecting Telerobot System , 2003 .

[9]  Weihua Sheng,et al.  Estimation of hand force from surface Electromyography signals using Artificial Neural Network , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[10]  Li Huijun,et al.  Virtual-Environment Modeling and Correction for Force-Reflecting Teleoperation With Time Delay , 2007 .

[11]  Hannes Bleuler,et al.  Visual and force feedback time-delays change telepresence: Quantitative evidence from crossmodal congruecy task , 2013, 2013 World Haptics Conference (WHC).

[12]  Huijun Li,et al.  Virtual-Environment Modeling and Correction for Force-Reflecting Teleoperation With Time Delay , 2007, IEEE Transactions on Industrial Electronics.

[13]  Klaus Landzettel,et al.  Predictive and knowledge-based telerobotic control concepts , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[14]  Claudio Melchiorri,et al.  A Comparison of Control Schemes for Teleoperation with Time Delay , 2001 .

[15]  Martin T. Hagan,et al.  Neural network design , 1995 .

[16]  Thomas B. Sheridan,et al.  Effects of predicted information in teleoperation with time delay , 1986 .

[17]  R. Held,et al.  Adaptation to displaced and delayed visual feedback from the hand. , 1966 .

[18]  Thomas B. Sheridan,et al.  Telerobotics, Automation, and Human Supervisory Control , 2003 .

[19]  F. Pfeiffer,et al.  Real-time simulation of non-smooth contacts in telepresence and teleaction applications , 2003, IEEE International Conference on Industrial Technology, 2003.

[20]  Jean-Jacques E. Slotine,et al.  Towards force-reflecting teleoperation over the Internet , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).