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.

To compensate for the lack of touch during minimally invasive and robotic surgeries, tactile sensors are integrated with surgical instruments. Surgical tools with tactile sensors have been used mainly for distinguishing among different tissues and detecting malignant tissues or tumors. Studies have revealed that malignant tissue is most likely stiffer than normal. This would lead to the formation of a sharp discontinuity in tissue mechanical properties. A hybrid piezoresistive-optical-fiber sensor is proposed. This sensor is investigated for its capabilities in tissue distinction and detection of a sharp discontinuity. The dynamic interaction of the sensor and tissue is studied using finite element method. The tissue is modeled as a two-term Mooney–Rivlin hyperelastic material. For experimental verification, the sensor was microfabricated and tested under the same conditions as of the simulations. The simulation and experimental results are in a fair agreement. The sensor exhibits an acceptable linearity, repeatability, and sensitivity in characterizing the stiffness of different tissue phantoms. Also, it is capable of locating the position of a sharp discontinuity in the tissue. Due to the simplicity of its sensing principle, the proposed hybrid sensor could also be used for industrial applications.

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.

Soft robots typically exhibit large deformation that makes integrating rigid sensors cumbersome. Especially for soft surgical robots, direct sensor-based feedback is required. In this study, we have proposed, modelled, prototyped, and validated a novel smart polymer-based soft sensor for integration with soft robots. Previously we have shown that the proposed smart polymer exhibits piezoresistivity. Thus, in this study, we integrated the proposed sensor with a flexural soft robot. Afterward, the sensor was calibrated through a series of experimental tests, and a multi-layer perceptron was trained for the calibration. The calibration showed a maximum root-mean-square error of 10.6 mN and a mean absolute error of 8±10 mN compared with the ground truth. The experimental validation showed that the proposed sensor and calibration method demonstrated a combined mean absolute error of 7.4±6.5 mN. In addition, the minimum detectable force of the sensor was less than 1 mN with a range of up to 284 mN. The system performance was compatible with representative intraluminal applications’ range and accuracy requirements.