ACAM-FoC: A Deep Neural Network Augmented From CAM-FoC to Measure the Grip Force of Mass-Produced Elongated Surgical Instruments

Abstract

Learning-based grip force measurement methods in robot-assisted minimally invasive surgery (RAMIS) outperforms the traditional model-based methods and avoids the application issues of sensor-based approaches. However, few studies have investigated the problem of grip force measurement in mass-produced surgical instruments. This letter takes the difference in motion hysteresis and mechanism friction among mass-produced surgical instruments into account, and proposes a novel learning-based method ACAM-FoC. ACAM-FoC is an augmentation of CAM-FoC, which is a high accuracy lightweight network proposed in our preceding research. Loss function monotonicity limitation is introduced to alleviate the negative effect of unbalance training data. Offiline experiments are conducted to verify the effectiveness and the advantages over other two existing methods. Online experiments on the self-developed surgical robot system are conducted to preliminarily realize and verify the grip force measurement and feedback in real time. The results support the importance of grip force feedback in leader-follower operation tasks.