Hyper-redundantmanipulators (HRMs) are widely used in minimally invasive surgery (MIS) because of their flexibility, dexterity, and compactness. However, when an external load acts on an HRM, its bending motion is disturbed and the workspace decreases. In addition, the external load increases the cable tension, and thin cables may break due to their low breaking strengths. This article proposes a cable-movable rolling joint that increases the workspace for MIS under an external load and reduces the actuating cable tension through cable movement. Furthermore, it presents an optimized motion estimation analysis method that verifies the manipulator performance by finding the equilibrium position and predicting the shape and tip position. The simulated manipulator exhibited a shape similar to that of the prototype and an average tip position difference of 4.59% based on the prototype length. Under an external load of 150 g, the workspace and cable tension of the prototype were 27.32% larger and 1.79 N lower than those of the conventional rolling joint, respectively. The new cable-movable mechanism and estimation analysis method will help improve the bending performance and position accuracy of HRMs for MIS.

This paper describes a surgical device that uses a compensation mechanism without motors and electronics to reduce the backlash in the end-effector. The device improves the precision and accuracy of surgical operations that involve unpredictable curved paths at a low cost. The device includes a flexible tube that moves along a curved path and a mechanism that compensates for the change in the length of the wire that actuates the end-effector as it follows an unpredictable path. The compensation mechanism minimizes the wire slack by utilizing a slider and spring mechanism that can maintain the pull wire length but can also move freely to extend the wire length as needed. We maximized the compensable wire length by optimizing the device driving parameters via simulation, verified the device performance experimentally, and built a fully operational prototype. The backlash of the device end-effector was reduced by approximately 38.3% compared to a device without compensation. Surgeons used the prototype with animal models in vivo and evaluated it as convenient and easy to manipulate. A hand-held non-robotic surgical device that compensates for wire length in unexpected curved paths to reduce end-effector backlash was demonstrated. The performance of the prototype was validated by driving, backlash, and hysteresis measurements, and in vivo animal experiments. This study is a significant step towards the development of a high-precision and low-cost surgical device for use in deep and narrow anatomies with unpredictable curved paths.

Hyper-redundant manipulators are widely used in minimally invasive surgery because they can navigate through narrow passages in passive compliance with the human body. Although their stability and dexterity have been significantly improved over the years, we need manipulators that can bend with appropriate curvatures and adapt to complex environments. This paper proposes a design principle for a manipulator capable of adjusting its non-uniform curvature and predicting the bending shape. Rigid segments were serially stacked, and elastic fixtures in the form of flat springs were arranged between hinged-slide joint segments. A manipulator with a diameter of 4.5 mm and a length of 28 mm had been fabricated. A model was established to predict the bending shape through minimum potential energy theory, kinematics, and measured stiffnesses of the flat springs. A comparison of the simulation and experimental results indicated an average position error of 3.82% of the endpoints when compared to the total length. With this modification, the manipulator is expected to be widely used in various fields such as small endoscope systems and single-port robot systems.

During the 2019 coronavirus disease pandemic, robotic-based systems for swab sampling were developed to reduce burdens on healthcare workers and their risk of infection. Teleoperated sampling systems are especially appreciated as they fundamentally prevent contact with suspected COVID-19 patients. However, the limited field of view of the installed cameras prevents the operator from recognizing the position and deformation of the swab inserted into the nasal cavity, which highly decreases the operating performance. To overcome this limitation, this study proposes a visual feedback system that monitors and reconstructs the shape of an NP swab using augmented reality (AR). The sampling device contained three load cells and measured the interaction force applied to the swab, while the shape information was captured using a motion-tracking program. These datasets were used to train a one-dimensional convolution neural network (1DCNN) model, which estimated the coordinates of three feature points of the swab in 2D X–Y plane. Based on these points, the virtual shape of the swab, reflecting the curvature of the actual one, was reconstructed and overlaid on the visual display. The accuracy of the 1DCNN model was evaluated on a 2D plane under ten different bending conditions. The results demonstrate that the x-values of the predicted points show errors of under 0.590 mm from $P_0$, while those of $P_1$ and $P_2$ show a biased error of about −1.5 mm with constant standard deviations. For the y-values, the error of all feature points under positive bending is uniformly estimated with under 1 mm of difference, when the error under negative bending increases depending on the amount of deformation. Finally, experiments using a collaborative robot validate its ability to visualize the actual swab’s position and deformation on the camera image of 2D and 3D phantoms.

Accurate nasopharyngeal specimen collection requires defining an appropriate insertion path, which is challenging under patient-specific facial anatomy and limited observation conditions. To address these challenges, this study presents an automated robotic framework that focuses on defining a patient-specific insertion path prior to the insertion process. The system uses an RGB-D camera and a deep learning model, EarPointNet, to detect facial landmarks, estimate the ear canal location, and compute a patient-specific insertion direction toward the nostril based on guidelines from the U.S. Centers for Disease Control and Prevention. EarPointNet achieved angular errors between 2.33° and 3.68° on an optimized dataset. Repeatability was further evaluated through comparative experiments with non-expert manual operation using a medical phantom. Additional experiments examined path estimation performance under partially occluded facial conditions with varying face-hole sizes, maintaining a mean angular error of 2.62° with a face hole radius of 100 mm. Future work will focus on integrating force sensing, torque sensing and reactive safety control, as well as conducting clinician-involved validation toward medical certification.