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.

Endovascular surgical robots (ESRs) are extensively researched because of their potential to minimize surgeons’ radiation exposure. However, during the surgical operation, it is difficult for ESR to achieve the same flexibility in manipulating the soft catheter and the slender guidewire as human fingers. The flexibility of ESR can be enhanced by incorporating a soft gripper, but the nonlinear deformation of soft material presents challenges in sensing and controlling the force. To address these issues, this study proposes a deep learning-based force sensing method that enables the flexible ESR to measure the surgical force and torque. The proposed deep learning model consists of multiple-layer Long Short-Term Memory (LSTM) modules and is trained using the robot’s motion and soft gripper deformation datasets. The well-trained LSTM model can predict the operationing force and torque in real time. The contrast experiments with other models demonstrate that our robot exhibits higher force measurement accuracy. Furthermore, a force control strategy is proposed for the application of LSTMbased ESR. In the force control strategy, the robot is encouraged to mimic the dexterity of surgeon’s fingers and to maintain the force within a safe range. Finally, the proposed strategy is compared with other strategies in vascular phantom experiments and is proven to effectively enhance the safety and efficiency of surgical operations.

Robot-assisted vascular interventional surgery changes the surgeons’ original operation, which affects their judgment and surgical safety. Therefore, the reliable safety method for robot-assisted surgery is practically valuable. However, the existing active safety methods have two challenges: the difficulty in setting inserting force thresholds suitable for the current vascular environment and the response delay caused by program processing. The paper improved a SEA(series elastic actuator)-based guidewire manipulator with an adjustable grasping force function. A multifunctional finger pulp was designed to measure the guidewire inserting force and perceive the manipulating state between fingers simultaneously. Then, an orthogonal force coupling model based on the mechanical-molecular friction theory was established to demonstrate the friction mechanism of cylindrical surgical instruments grasped by elastic grasping surfaces. Based on the model, a passive safety method was proposed to adjust the grasping force autonomously, which can satisfy the inserting force requirements of the phase delivery task and control the safety threshold suitable for the current environment. The evaluation experimental results demonstrated that the designed robot system could manipulate the guidewire accurately. Then the friction experiments verified the designed orthogonal force coupling model. Finally, the in vitro experiments were performed to demonstrated that the designed passive safety method can automatically adjust the grasping force based on the inserting states of the guidewire, reduce the contact force with the vessels, and ensure surgical safety in real-time.

Endovascular surgery is a high-risk operation with limited vision and intractable guidewires. At present, endovascular surgery robot (ESR) systems based on force feedback liberates surgeons’ operation skills, but it lacks the ability to combine force perception with vision. In this study, a deep learning-based guidewire-compliant control method (GCCM) is proposed, which guides the robot to avoid surgical risks and improve the efficiency of guidewire operation. First, a deep learning-based model called GCCM-net is built to identify whether the guidewire tip collides with the vascular wall in real time. The experimental results in a vascular phantom show that the best accuracy of GCCM-net is 94.86 ± 0.31%. Second, a real-time operational risk classification method named GCCM-strategy is proposed. When the surgical risks occur, the GCCM-strategy uses the result of GCCM-net as damping and decreases the robot’s running speed through virtual resistance. Compared with force sensors, the robot with GCCM-strategy can alleviate the problem of force position asynchrony caused by the long and soft guidewires in real-time. Experiments run by five guidewire operators show that the GCCM-strategy can reduce the average operating force by 44.0% and shorten the average operating time by 24.6%; therefore the combination of vision and force based on deep learning plays a positive role in improving the operation efficiency in ESR.

The flexible grasping, force sensing, and in-hand manipulating abilities of the gripper are theoretically significant and practically valuable to solve. However, it is still difficult for most existing grippers to realize these three functions simultaneously. This paper proposed a novel humanoid finger-functional parallel gripper with two actuators based on overlapping parallelogram mechanisms and the series elastic actuator (SEA). Based on the in-hand manipulation principle for the ordinary contour object, an in-hand manipulation algorithm for grasping force and position decoupling was designed. Then, to adjust the posture and position of objects within parallel fingers, the rolling and sliding conditions of objects based on the molecular-mechanical friction theory were analyzed. Combined with the proposed in-hand manipulating conditions and algorithm, the PG2 gripper can control the grasping force and manipulate the ordinary contour object in-hand controllably. Evaluation experimental results showed that the force-position control of the PG2 gripper was decoupled stably and the gripper had high accuracy in controlling force and position. Finally, the physical features perception experiments and the in-hand manipulating experiment were carried out to demonstrate the utility of the proposed manipulating methods and algorithm, and in-hand manipulating and grasping force sensing abilities of the gripper. The possible applications of this gripper include tasks that need accurate force-position control or manipulating and sensing in-hand remotely.

Research on endovascular surgery robots (ESR) is continuously developing, because ESR can protect surgeons from radiation exposure. For designing an ESR manipulator, the main challenge is controlling the soft surgical tools and measuring the endovascular stress simultaneously. In this paper, a flexible tactile-sensing gripper (FTG) is designed to realize the stable grasping of the surgical catheter and the tactile sensing of catheter force. Firstly, a catheter grasping model was constructed, and the factors affecting the force measurement were quantita-tively analyzed. Secondly, the simulation experiments based on FTG models with three different sizes were implemented. When the catheter force was too large, shrinking the grasping distance of FTG can avoid the surgical risk. This method protected the surgeon's behavior and controlled the catheter force at the same time, which was called excessive force protection function (EFPF). Thirdly, based on simulation results, we made a FTG prototype which meet the surgical requirements and integrated it into an ESR manipulator. This manipulator could measure the catheter forces by detecting the coordinate change of marking points on FTG surface. The calibrated FTG prototype got average and maximum errors of force sensing approximately 37 mN and 223 mN, respectively. Finally, the proposed EFPF is evaluated. In the experiment of carotid artery catheterization, EFPF controlled the catheter force within 393 mN, which was far less than the control group's 1351 mN.