Learning-Based High-Precision Force Estimation and Compliant Control for Small-Scale Continuum Robot

Small-scale continuum robot-assisted minimally invasive surgery has received crucial attention due to its smaller incisions and high dexterity. In medical scenarios such as radiofrequency ablation and nasal/throat swab sampling, monitoring and controlling the forces applied to human tissue can help improve the safety and comfort level of the procedure. However, the tip-sensor-based force detection method can barely be deployed due to the miniature size of the continuum robot; meanwhile, the mechanical modeling-based high-precision force estimation cannot be realized on account of the continuum robots' complex structure with high nonlinear properties. To address the high-precision force estimation challenge for further compliant control during minimally invasive interventions, a learning-based high-precision force estimation method via long short-term memory (LSTM) is proposed in this paper. On this basis, compliance control and high-precision force tracking can be further realized for small-scale continuum robot. The compliance control ensures a smooth and stable transition during the interaction between the robot and the environment, and force tracking can be utilized for maintaining or precisely controlling the force applied to the human tissue. Finally, the contact force sensing and control experiments are carried out on a small-scale continuum robot system prototype, and a demonstration using a human nasal cavity model is conducted. The results validate that the proposed LSTM neural network fits the mechanical model of the continuum robot well with the root mean square error of 3.44mN, and the control method can significantly compensate for the instantaneous impact during contact with an attenuation of 59.3% and rapidly respond to keep the force accurately at the expected value with the mean absolute error of 2.41mN.