An Intravascular Catheter Bending Recognition Method for Interventional Surgical Robots

Robot-assisted interventional surgery can greatly reduce the radiation received by surgeons during the operation, but the lack of force detection and force feedback is still a risk in the operation which may harm the patient. In those robotic surgeries, the traditional force detection methods may have measurement losses and errors caused by mechanical transmission and cannot identify the direction of the force. In this paper, an interventional surgery robot system with a force detection device is designed and a new force detection method based on strain gauges is proposed to detect the force and infer the bending direction of the catheter in the vessel by using BP neural network. In addition, genetic algorithm is used to optimize the BP neural network, and the error between the calculated results and the actual results is reduced by 37%, which improves the accuracy of catheter bending recognition. Combining this new method with traditional force sensors not only reduces the error caused by the traditional mechanical transmission, but also can detect the bending direction of the catheter in the blood vessel, which greatly improves the safety of the operation.

Drosophila Mechanical Nociceptors Preferentially Sense Localized Poking

Mechanical nociception is an evolutionarily conserved sensory process required for the survival of living organisms. Previous studies have revealed much about the neural circuits and key sensory molecules in mechanical nociception, but the cellular mechanisms adopted by nociceptors in force detection remain elusive. To address this issue, we study the mechanosensation of a fly larval nociceptor (class IV da neurons, c4da) using a customized mechanical device. We find that c4da are sensitive to mN-scale forces and make uniform responses to the forces applied at different dendritic regions. Moreover, c4da showed a greater sensitivity to more localized forces, consistent with them being able to sense the poking of sharp objects, such as wasp ovipositor. Further analysis reveals that high morphological complexity, mechanosensitivity to lateral tension and active signal propagation in the dendrites altogether facilitate the mechanosensitivity and sensory features of c4da. In particular, we discover that Piezo and Ppk1/Ppk26, two key mechanosensory molecules, make differential but additive contributions to the mechanosensation of c4da. In all, our results provide updates into understanding how c4da process mechanical signals at the cellular level and reveal the contributions of key molecules.

A large-area flexible tactile sensor for multi-touch and force detection using electrical impedance tomography

In this paper, a large-area flexible tactile sensor for multi-touch and force detection based on EIT technology was developed. A novel design of a sensor material made of a porous elastic polymer and ionic liquid was proposed. The proposed conductive flexible materials combining elastic porous structures and conductive liquids provide continuous, linear changes in impedance with respect to touch forces. A deep learning scheme PSPNet based on MobileNet was adopted to postprocess the originally reconstructed images to improve the performance of tactile perception. By using this data-driven method, we can improve the spatial resolution of the tactile sensor to achieve a single-point position detection error of 7.5±4.5 mm without using internal electrodes.

A large-area flexible tactile sensor for multi-touch and force detection using electrical impedance tomography

In this paper, a large-area flexible tactile sensor for multi-touch and force detection based on EIT technology was developed. A novel design of a sensor material made of a porous elastic polymer and ionic liquid was proposed. The proposed conductive flexible materials combining elastic porous structures and conductive liquids provide continuous, linear changes in impedance with respect to touch forces. A deep learning scheme PSPNet based on MobileNet was adopted to postprocess the originally reconstructed images to improve the performance of tactile perception. By using this data-driven method, we can improve the spatial resolution of the tactile sensor to achieve a single-point position detection error of 7.5±4.5 mm without using internal electrodes.

Realization of Force Detection and Feedback Control for Slave Manipulator of Master/Slave Surgical Robot

Robot-assisted minimally invasive surgery (MIS) has received increasing attention, both in the academic field and clinical operation. Master/slave control is the most widely adopted manipulation mode for surgical robots. Thus, sensing the force of the surgical instruments located at the end of the slave manipulator through the main manipulator is critical to the operation. This study mainly addressed the force detection of the surgical instrument and force feedback control of the serial surgical robotic arm. A measurement device was developed to record the tool end force from the slave manipulator. An elastic element with an orthogonal beam structure was designed to sense the strain induced by force interactions. The relationship between the acting force and the output voltage was obtained through experiment, and the three-dimensional force output was decomposed using an extreme learning machine algorithm while considering the nonlinearity. The control of the force from the slave manipulator end was achieved. An impedance control strategy was adopted to restrict the force interaction amplitude. Modeling, simulation, and experimental verification were completed on the serial robotic manipulator platform along with virtual control in the MATLAB/Simulink software environment. The experimental results show that the measured force from the slave manipulator can provide feedback for impedance control with a delay of 0.15 s.

Sensitive detection of cell-derived force and collagen matrix tension in microtissues undergoing large-scale densification

Mechanical forces generated by cells and the tension of the extracellular matrix (ECM) play a decisive role in establishment, homeostasis maintenance, and repair of tissue morphology. However, the dynamic change of cell-derived force during large-scale remodeling of soft tissue is still unknown, mainly because the current techniques of force detection usually produce a nonnegligible and interfering feedback force on the cells during measurement. Here, we developed a method to fabricate highly stretchable polymer-based microstrings on which a microtissue of fibroblasts in collagen was cultured and allowed to contract to mimic the densification of soft tissue. Taking advantage of the low-spring constant and large deflection range of the microstrings, we detected a strain-induced contraction force as low as 5.2 µN without disturbing the irreversible densification. Meanwhile, the microtissues displayed extreme sensitivity to the mechanical boundary within a narrow range of tensile stress. More importantly, results indicated that the cell-derived force did not solely increase with increased ECM stiffness as previous studies suggested. Indeed, the cell-derived force and collagen tension exchanged dramatically in dominating the microtissue strain during the densification, and the proportion of cell-derived force decreased linearly as the microtissue densified, with stiffness increasing to ∼500 Pa. Thus, this study provides insights into the biomechanical cross-talk between the cells and ECM of extremely soft tissue during large-extent densification, which may be important to guide the construction of life-like tissue by applying appropriate mechanical boundary conditions.

Fiber-tip polymer clamped-beam probe for high-sensitivity nanoforce measurements

Micromanipulation and biological, material science, and medical applications often require to control or measure the forces asserted on small objects. Here, we demonstrate for the first time the microprinting of a novel fiber-tip-polymer clamped-beam probe micro-force sensor for the examination of biological samples. The proposed sensor consists of two bases, a clamped beam, and a force-sensing probe, which were developed using a femtosecond-laser-induced two-photon polymerization (TPP) technique. Based on the finite element method (FEM), the static performance of the structure was simulated to provide the basis for the structural design. A miniature all-fiber micro-force sensor of this type exhibited an ultrahigh force sensitivity of 1.51 nm μN−1, a detection limit of 54.9 nN, and an unambiguous sensor measurement range of ~2.9 mN. The Young’s modulus of polydimethylsiloxane, a butterfly feeler, and human hair were successfully measured with the proposed sensor. To the best of our knowledge, this fiber sensor has the smallest force-detection limit in direct contact mode reported to date, comparable to that of an atomic force microscope (AFM). This approach opens new avenues towards the realization of small-footprint AFMs that could be easily adapted for use in outside specialized laboratories. As such, we believe that this device will be beneficial for high-precision biomedical and material science examination, and the proposed fabrication method provides a new route for the next generation of research on complex fiber-integrated polymer devices.

Influence of Configuration Error in Bolted Joints on Detection Error of Clamp Force Detection Method

Clamp force errors in bolted joints often cause accidents in various mechanical structures. Therefore, the clamp force must be controlled accurately and maintained for securing the reliability of mechanical structures such as vehicles. However, the clamp force cannot be controlled easily during tightening. Moreover, it is difficult to detect the clamp force after tightening. We previously proposed a method to easily detect the clamp force of a bolted joint that has been tightened. In that method, the bolt thread protruding from the nut is pulled while the nut’s upper surface is supported. The relationship between tensile force and displacement at the pulling point where the tensile force is applied differs before and after the tensile force reaches the clamp force. The method detects the tensile force at the point, where the relationship changes, as the clamp force. In this study, we investigate the influence of squareness error on the bearing surface of the clamped part in a bolted joint on the detection error of the method using experiments and finite element (FE) analysis. The experimental results show that the squareness error has an influence on the detection accuracy. The average detection error in the experiments increases by approximately 10% with an increase in the squareness error. To understand the cause of this phenomenon, we investigate the effects of backlash between mating thread surfaces of bolts and nuts on the detection error. The results show that the error decreased because of the backlash. Consequently, it is assumed that the error is caused by the non-separation of the mating thread surfaces when the tensile force reached the clamp force. Furthermore, the FE analysis results show that the squareness error on the bearing surface of the clamped part has an influence of the squareness error on the detection accuracy. The results indicate that we should control the tolerance of squareness errors on the bearing surface of the clamped part when the clamp force detection method is applied to bolted joints.