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Improving Haptic Transparency for Uncertain Virtual Environments Using Adaptive Control and Gain-Scheduled

Shane Forbrigger, Ya-Jun Pan

IEEE Transactions on Haptics

|July 12, 2018

Summary

Haptic interfaces in surgical training require high-speed virtual environments. This study introduces a gain-scheduled predictor to improve transparency for complex tissue models, enhancing realism in minimally invasive surgery (MIS) simulators.

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Area of Science:

Robotics
Virtual Reality
Surgical Simulation

Background:

Haptic interfaces are crucial for realistic training in fields like minimally invasive surgery (MIS).
High update rates are essential for transparent virtual environments (VEs) in surgical simulators.
Complex, deformable tissue models present computational challenges, hindering high update rates.

Purpose of the Study:

To enhance VE transparency for complex tissue models in haptic simulations.
To formulate nonlinear dynamics as a quasi-linear parameter varying (LPV) system.
To design a predictor for higher output update rates in haptic feedback systems.

Main Methods:

Formulating nonlinear tissue dynamics as a quasi-linear parameter varying (LPV) system.
Designing a gain-scheduled predictor using Lyapunov-based methods and linear matrix inequalities.
Implementing an adaptive controller for nonlinear, delayed, and sampled virtual environments.

Main Results:

The gain-scheduled predictor significantly improved performance compared to systems without prediction.
Experimental results validated the effectiveness of the proposed gain-scheduled predictor approach.
Performance improvements with the gain-scheduled predictor were less significant than with a constant-gain predictor.

Conclusions:

Gain-scheduled prediction offers a viable method to enhance haptic feedback transparency in complex surgical simulations.
The approach effectively addresses the computational challenges of nonlinear tissue dynamics.
Further research may refine the predictor for even greater performance gains in haptic training systems.