A master-slave vascular interventional surgical robotic system (MSVISRs) promises to treat cardio-cerebrovascular diseases without damaging the health of surgeons. Although many scholars are engaged in the research on the slave manipulator for the MSVISRs, there are very few studies on the master manipulator. In this study, a surgeon’s habits-based novel master manipulator is developed. This master manipulator imitates the operating habits of surgeons in surgery and uses a non-contact method with an optical mouse sensor to measure the operating displacements. The novelty of this approach is that this master manipulator with smaller size and non-contact displacement measurement method can provide the axial force and the circumferential force feedback independently. In this master manipulator, the rigid-flexible coupling structure is used to provide the axial force feedback. In addition, the brush DC motor is used to give the circumferential force feedback. Furthermore, the proposed master manipulator is calibrated and evaluated by experiments. The calibration experiments show that the maximum identification ranges for the axial force and circumferential force in the proposed master manipulator are 0.247-4.5N and 0.52-4.3N, respectively. Moreover, the evaluation experiments show that the maximum error is 0.8 mm in the axial direction. As compared with other master manipulator, the comprehensive properties of this novel master manipulator are shown to be better and more promising.

In vascular interventional surgery, surgeons operate guidewires and catheters to diagnose and treat patients with the assistance of the digital subtraction angiography (DSA). Therefore, the surgeon will be exposed to X-rays for extended periods. To protect the surgeon, the development of a robot-assisted surgical system is of great significance. The displacement tracking accuracy is the most important issue to be considered in the development of the system. In this study, the active disturbance rejection control (ADRC) method is applied to guarantee displacement tracking accuracy. First, the core contents of the proportional–integral–derivative (PID) and ADRC methods are analyzed. Second, comparative evaluation experiments for incremental PID and ADRC methods are presented. The results show that the ADRC method has better performance of than that of the incremental PID method. Finally, the calibration experiments for the ADRC control method are implemented using the master–slave robotic system. These experiments demonstrate that the maximum tracking error is 0.87 mm using the ADRC method, effectively guaranteeing surgical safety.

Vascular interventional surgery is a typical method for diagnosing and treating cardio-cerebrovascular diseases. However, a surgeon is exposed to significant X-radiation exposure when the operation is conducted for a long period of time. A vascular intervention surgical robotic system for assisting the surgeon is a promising approach to address the aforementioned issue. When developing the robotic system, a high displacement accuracy is crucial, and this can aid in enhancing operating efficiency and safety. In this study, a novel kinetics analysis and active disturbance rejection control (ADRC)-based controller is proposed to provide high accuracy for a string-driven robotic system. In this controller, kinetics analysis is initially used to improve the accuracy affected by the inner factors of the slave manipulator. Then, the ADRC controller is used to further improve the operating accuracy of the robotic system. Finally, the proposed controller is evaluated by conducting experiments on a vascular model. The results indicate maximum steady errors of 0.45 mm and 6.67°. The experimental results demonstrate that the proposed controller can satisfy the safety requirements of the string-driven robotic system.

The virtual training system of vascular interventional surgery can improve the operation level of novice interventional surgeons and reduce the training cost. The deformation simulation of vascular tissue is the key part of the virtual training system for vascular interventional surgery. This paper presents a vascular tissue model based on nonlinear viscoelastic tensor-mass method (NVTMM), which can realize real-time visual feedback and haptic feedback. The hyperelastic model is used to simulate the nonlinear viscoelastic behavior of blood vessels. Meanwhile, the Euler's implicit integration scheme is used to make the simulation results more stable. In addition, a haptic force feedback device is introduced for the interaction between the vascular model and the virtual medical instrument, which improves the authenticity of the simulation. Finally, the real-time performance and accuracy of NVTMM, MSM and FEM are compared through experiments. The results show that NVTMM method has high real-time performance and can reflect the nonlinear viscoelastic behavior of blood vessels to a certain extent.