Minimally invasive surgery has been one of the most significant evolutions in medicine. In this approach, the surgeon inserts specially-designed instruments through a small incision on the patient’s skin into the body cavities, abdomen, veins or, arteries and performs the surgery on organs. As a major limitation, surgeons lose their natural tactile perception due to indirect touch on the organs. Since the loss of tactile perception compromises the ability of surgeons in tissue distinction and maneuvers, researchers have proposed different tactile sensors. This review is to provide researchers with a literature map for the state-of-the-art of tactile sensors in minimally invasive surgery, e.g. in robotic, laparoscopic, palpation, biopsy, heart ablation, and valvuloplasty. In this regard, the pertinent literature from the year 2000 on sensing principles, design requirements, and specifications were reviewed in this study. The survey showed that size, range, resolution, variation, electrical passivity, and magnetic-resonance-compatibility were the most critical specification to study for tactile sensors. Based on the results, some of the requirements, e.g., magnetic-resonance-compatibility and electrical passivity are of less generality and more application-dependent; however, size, resolution, and range specifications differ for various applications and are of utmost importance.

Cardiac diseases are recognized as the leading cause of mortality, hospitalization, and medical prescription globally. The gold standard for the treatment of coronary artery stenosis is the percutaneous cardiac intervention which is performed under live X-ray imaging. Substantial clinical evidence shows that the surgeon and staff are prone to serious health problems due to X-ray exposure and occupational hazards. Telerobotic vascular intervention systems with a master-slave architecture reduced the X-ray exposure and enhanced the clinical outcomes; however, the loss of haptic feedback during surgery has been the main limitation of such systems. This study is a review of the state-of-the-art for haptic telerobotic cardiovascular interventions. A survey on the literature published between 2000 to 2019 was performed. Results of the survey were screened based on their relevance to this work. Also, the leading research disciplines were identified based on the results of the survey. Furthermore, different approaches for haptic telerobotic cardiovascular intervention and their relevant methods were critically reviewed and compared, and finally, the current limitations of the state-of-the-art, unexplored research areas as well as the future perspective of the research on this technology were laid out.

The goal of this study was to propose and validate a control framework with level-2 autonomy (task autonomy) for the control of flexible ablation catheters. To this end, a kinematic model for the flexible portion of typical ablation catheters was developed and a 40-mm-long spring-loaded flexible catheter was fabricated. The feasible space of the catheter was obtained experimentally. Furthermore, a robotic catheter intervention system was prototyped for controlling the length of the catheter tendons. The proposed control framework used a support vector machine classifier to determine the tendons to be driven, and a fully connected neural network regressor to determine the length of the tendons. The classifier and regressors were trained with the data from the feasible space. The control system was implemented in parallel at user-interface and firmware and exhibited a 0.4-s lag in following the input. The validation studies were four trajectory tracking and four target reaching experiments. The system was capable of tracking trajectories with an error of 0.49 ± 0.32 and 0.62 ± 0.36 mm in slow and fast trajectories, respectively. Also, it exhibited submillimeter accuracy in reaching three preplanned targets and ruling out one nonfeasible target autonomously. The results showed improved accuracy and repeatability of the position control compared with the recent literature. The proposed learning-based approach could be used in enabling task autonomy for catheter-based ablation therapies.

simple and miniaturized optical tactile sensor for integrating with robotic and manual minimally invasive surgery graspers is proposed in this study. For better miniaturization, the sensing principle of constant-bending-radius light intensity modulation was replaced with a variable-bending-radius modulation principle, and the pertinent theoretical formulation was derived. Afterward, a finite element model of the sensor was optimized using response surface optimization technique. The optimized sensor design was 14:0 mm long, 1:8 mm wide and 4 mm high. Next, the sensor was prototyped using SLA 3D printing technique. Also, the sensor was calibrated using a rate-dependent learning-based support-vector-regression algorithm. Calibration was 96% linear with a goodness-of-fit of 93% and mean absolute error of 0:085 ± 0:096 N. Furthermore, the sensor was tested under cyclic triangular compression with a 3 sec pause between loading and unloading as well as manual grasping. Mean absolute error of 0:12 ± 0:08 N, the minimum force of 0:14 N, and repeatability of 0:07 N showed the acceptable performance of the proposed sensor for surgical applications. Moreover, the sensor showed the capability of working under combined dynamic and static loading conditions with low hysteresis, i.e., 0:057 N=cycle.

The spatial orientation of rotating objects is typically measured by utilizing inertial measurement units and requires temporal integration of angular velocities. The integration of the angular velocities accumulates the measurement noise and results in erroneous orientation estimation, thus, necessitates real-time error compensation. In this article, an integral-free 3-D orientation estimation framework based on stereo-accelerometry and sensor fusion is proposed and validated. Afterward, a wearable device, MiCarp for robot-assisted interventional surgery was designed, prototyped, and investigated for the accuracy and real-time performance. To achieve the real-time performance, an artificial neural network was trained and implemented in the wearable device. The comparison of the results of the proposed method with a representative complementary filtering method showed the superior performance. The proposed method had a mean-absolute-error of $1.7^\circ \pm 2.4^\circ$ , a measurement range of $\pm 180^\circ$ , and a real-time sample rate of up to 117 Hz. In the end, the feasibility of integrating the proposed device with a representative robotic intervention system was investigated. The proposed wearable device showed the capability of robust capturing of multiple successive rotations for an arterial cannulation task on a vascular model.

Soft robots have gained popularity, especially in intraluminal applications, because their soft bodies make them safer for surgical interventions than flexures with rigid backbones. This study investigates a pressure-regulating stiffness tendon-driven soft robot and provides a continuum mechanics model for it towards using that in adaptive stiffness applications. To this end, first, a central single-chamber pneumatic and tri-tendon-driven soft robot was designed and fabricated. Afterward, the classic Cosserat’s rod model was adopted and augmented with the hyperelastic material model. The model was then formulated as a boundary-value problem and was solved using the shooting method. To identify the pressure-stiffening effect, a parameter-identification problem was formulated to identify the relationship between the flexural rigidity of the soft robot and internal pressure. The flexural rigidity of the robot at various pressures was optimized to match theoretical deformation and experiments. The theoretical findings of arbitrary pressures were then compared with the experiment for validation. The internal chamber pressure was in the range of 0 to 40 kPa and the tendon tensions were in the range of 0 to 3 N. The theoretical and experimental findings were in fair agreement for tip displacement with a maximum error of 6.40% of the flexure’s length.