TCN-Based Distal Force Feedback Strategy of a Vascular Interventional Surgery Robot

Vascular interventional surgery (VIS) robot is a surgical treatment plan that effectively protects surgeons from X-ray radiation. However, the master–slave control method cuts off the surgeons’ natural force feedback, which increases the risk of surgical safety. Most VIS robotic systems use force sensors placed at the proximal end of the guidewire to achieve force feedback, but due to the nonrigidity of the guidewire and the influence of mechanism friction, the proximal force collected has certain errors. In addition, the current VIS robotic systems are also insufficient in functionality and cannot simultaneously complete the delivery of multiple surgical instruments. To solve the above mechanism design and force feedback challenges, a novel VIS robotic system equipped with a force-sensing mechanism is developed in this study. In addition, a temporal convolutional network (TCN) for the guidewire distal force prediction and an enhanced interactive force feedback strategy (EIFFS) are proposed to improve the safety of the robotic system. Finally, combining the developed robotic system and the EIFFS, a series of performance evaluations and model experiments are carried out. The results of the study demonstrate the effectiveness of the developed robotic system and the feasibility of the EIFFS in improving surgical safety.