Refined Motion Compensation with Soft Laser Manipulators using Data-Driven Surrogate Models

TL;DR

This paper presents a data-driven surrogate modeling approach for soft laser manipulators to improve motion compensation during laser ablation surgeries, enhancing control efficiency and adaptability.

Contribution

It introduces a novel data-driven surrogate model using spectral submanifolds for soft manipulators, integrated into an MPC framework for improved motion compensation.

Findings

Surrogate models enable efficient control of soft manipulators.

MPC with surrogate models outperforms other models in motion compensation.

Supports design-agnostic interchangeability of manipulators.

Abstract

Non-contact laser ablation, a precise thermal technique, simultaneously cuts and coagulates tissue without the insertion errors associated with rigid needles. Human organ motions, such as those in the liver, exhibit rhythmic components influenced by respiratory and cardiac cycles, making effective laser energy delivery to target lesions while compensating for tumor motion crucial. This research introduces a data-driven method to derive surrogate models of a soft manipulator. These low-dimensional models offer computational efficiency when integrated into the Model Predictive Control (MPC) framework, while still capturing the manipulator's dynamics with and without control input. Spectral Submanifolds (SSM) theory models the manipulator's autonomous dynamics, acknowledging its tendency to reach equilibrium when external forces are removed. Preliminary results show that the MPC controller using the surrogate model outperforms two other models within the same MPC framework. The data-driven MPC controller also supports a design-agnostic feature, allowing the interchangeability of different soft manipulators within the laser ablation surgery robot system.