AI-Powered Robust Interaction Force Control of a Cardiac Ultrasound Robotic System

Abstract

This article introduces a novel intelligent robust interaction force control method for a cardiac ultrasound robotic system (CURS), exploiting dual control loops and artificial intelligence (AI)-driven image feedback to enhance both image quality and patient safety during cardiac examinations. Unlike existing systems that use a constant interaction force, the proposed method adjusts the force based on ultrasound image feedback, which is critical for adapting to different cardiac views. The system employs an internal control loop, where the force feedback generates control commands (low-level controller), and an external control loop, where the feedback is processed through a convolutional neural network (CNN), named ultrasound-cardiac-feature-net (UCF-Net), determines the optimal force values (high-level controller). An adaptive filtered quasi-sliding mode controller (AFQSMC) manages both interaction force and probe’s position within a hybrid position/force control context, ensuring robustness against uncertainties and disturbances. Experimental evaluations on a cardiac phantom navigating main cardiac views demonstrate the superiority of the proposed approach over traditional constant force control. Moreover, AFQSMC achieves significant improvements in interaction force control, with enhancements ranging from 21.87% to 68.25% over traditional FQSMC, sliding mode control (SMC), and proportional-integral (PI) controllers, across quantitative metrics such as root mean square (RMS), standard deviation (STD), and Max, confirming its potential for improving cardiac examination performance.