Improved Transparency for Haptic Systems with Complex Environments

Abstract Haptic systems in surgical training applications require highly accurate force feedback. However, high-resolution models of the virtual environment (VE) can be very computationally intensive, lowering the force feedback update rate. The objective of this work is to improve transparency by developing a predictor that approximates the complex nonlinear VE as a linear VE with a much higher update rate. By using feedback from the more accurate but slower VE, the predictor can provide increased transparency to the operator. The full control design of the predictor and haptic controller is considered for a nonlinear haptic device. The predictor is designed using Lyapunov-based methods, by numerical solution of a linear matrix inequality. The predictor uses a projection-type adaptation law to estimate the unknown VE parameters. Simulation results are shown to demonstrate the effectiveness of the method assuming unknown and time-varying VE parameters.

[1]  Ya-Jun Pan,et al.  Control gain design for bilateral teleoperation systems using linear matrix inequalities , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[2]  Yang Shi,et al.  Transparent Virtual Coupler Design for Networked Haptic Systems With a Mixed Virtual Wall , 2012, IEEE/ASME Transactions on Mechatronics.

[3]  Paolo Fiorini,et al.  A unified representation to interact with simulated deformable objects in virtual environments , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[4]  John Kenneth Salisbury,et al.  Stability of Haptic Rendering: Discretization, Quantization, Time Delay, and Coulomb Effects , 2006, IEEE Transactions on Robotics.

[5]  Stefano Stramigioli,et al.  Contact impedance estimation for robotic systems , 2005, IEEE Trans. Robotics.

[6]  Shahin Sirouspour,et al.  Adaptive Control for Improved Transparency in Haptic Simulations , 2009, IEEE Transactions on Haptics.

[7]  William Z Rymer,et al.  Investigation of Soft-Tissue Stiffness Alteration in Denervated Human Tissue Using an Ultrasound Indentation System , 2008, The journal of spinal cord medicine.

[8]  Bin Yao,et al.  Integrated direct/indirect adaptive robust control of SISO nonlinear systems in semi-strict feedback form , 2003, Proceedings of the 2003 American Control Conference, 2003..

[9]  Anatole Lécuyer,et al.  Enhancing Audiovisual Experience with Haptic Feedback: A Survey on HAV , 2013, IEEE Transactions on Haptics.

[10]  Xiaojun Shen,et al.  Motion and Force Prediction in Haptic Media , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[11]  James Edward Colgate,et al.  Passivity of a class of sampled-data systems: application to haptic interfaces , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[12]  Ya-Jun Pan,et al.  Adaptive robust control of bilateral teleoperation systems with unmeasurable environmental force and arbitrary time delays , 2014 .

[13]  Christopher R. Wagner,et al.  Mechanisms of performance enhancement with force feedback , 2005, First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics Conference.

[14]  Olga Sourina,et al.  A Prediction Method Using Interpolation for Smooth Six-DOF Haptic Rendering in Multirate Simulation , 2013, 2013 International Conference on Cyberworlds.

[15]  Eckehard G. Steinbach,et al.  Hybrid signal-based and geometry-based prediction for haptic data reduction , 2011, 2011 IEEE International Workshop on Haptic Audio Visual Environments and Games.

[16]  Said Mammar,et al.  A Smith-prediction based haptic feedback controller for time delayed virtual environments systems , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).