Model Predictive Path Integral Control of I²RIS Robot Using RBF Identifier and Extended Kalman Filter.

Area of Science:

• Robotics
• Medical Robotics
• Control Systems

Background:

• Controlling cable-driven snake robots, especially for ophthalmic surgery (e.g., I²RIS), is complex due to nonlinearities like hysteresis and friction.
• Small-scale robots like I²RIS lack sensory feedback, increasing control challenges in delicate procedures.

Purpose of the Study:

• To develop and evaluate an adaptive control strategy for data-driven models of ophthalmic robotic snakes.
• To improve the robustness and performance of robotic snake control in the face of model uncertainties and unknown environments.

Main Methods:

• Applied a Model Predictive Path Integral (MPPI) controller to a Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) based data-driven model of the I²RIS.
• Simulated unknown external disturbances and environmental loads to test performance in unseen scenarios.
• Implemented online uncertainty identification using a Radial Basis Function (RBF) network with weights updated by an Extended Kalman Filter (EKF).

Main Results:

• The MPPI controller demonstrated robust optimal control solutions even with simulated uncertainties.
• The adaptive mechanism effectively identified and compensated for model uncertainties online.
• MPPI showed computational superiority compared to conventional Model Predictive Control (MPC).

Conclusions:

• The proposed adaptive MPPI controller enhances the reliability of data-driven models for ophthalmic robotic snakes.
• This approach offers a computationally efficient and robust solution for controlling robotic snakes in complex surgical environments.
• The integration of GMM-GMR, RBF, and EKF provides a powerful framework for adaptive robotic control.