In this paper, a one-dimensional distal force sensor design is proposed for minimally invasive surgery based on fiber Bragg grating (FBG) sensing principle. The sensor is made with a miniaturized force-sensitive flexure within which an optical fiber is embedded. The former includes a spiral elastomer utilized for high axial force sensitivity, while the fiber includes dual FBGs that are tightly suspended around the distal parts of the flexure and along elastomer’s centerline, respectively. Theoretical model of the flexure was derived based on its components and a design approach for decoupling strain and temperature cross-effects during sensor usage. In addition, design parameters of the flexure were analyzed on physics-based model optimization using fmincon function, while the static and dynamic properties were analyzed in-silico using finite element method. Further experiments were also carried out to analyze the sensor’s performance, and the data obtained was used for calibration study. The latter was done by training a feed-forward neural network designed for predicting the force vs. wavelength-shift relationship. The experimental results show the designed force sensor design can sense force values in a range of 1N with an average relative error less than 2% of full scale. Ex-vivo tissue palpation experiments were further done on pig liver organs to verify the effectiveness and applicability of the proposed sensor design in minimally invasive surgery.

The emerging demand for bio-inspired soft robotics requires novel soft actuators whose performance exceeds conventional rigid ones. Dielectric elastomer actuators (DEAs) are a promising soft actuation technology with large actuation strain and fast response. Cone DEAs are one of the most widely adopted DEA configurations for their compact structure and large force/stroke output with several configuration variations developed in recent years. By driving at a resonant frequency, the cone DEAs show a significant amplification in their power outputs, which demonstrates their suitability for highly dynamic robotic applications. However, it is still unclear how the payload conditions could affect the power outputs of cone DEAs and no work has compared the output performance of different variations of cone configurations. In this work, by considering conical configuration DEAs with generalized dissipative payloads, we conduct an extensive study on the effects of payload conditions on the power outputs of the cone DEA family. Additionally, we benchmark the performance of different cone DEA configurations and illustrate the fundamental principles behind these output patterns. The findings reported in this work establish guidelines for designing high-performance cone DEA actuators.

Recently, flexible tactile sensors based on three-dimensional (3D) porous conductive composites, endowed with high sensitivity, a wide sensing range, fast response, and the capability to detect low pressures, have aroused considerable attention. These sensors have been employed in different practical domain areas such as artificial skin, healthcare systems, and human–machine interaction. In this study, a facile, cost-efficient method is proposed for fabricating a highly sensitive piezoresistive tactile sensor based on a 3D porous dielectric layer. The proposed sensor is designed with a simple dip-coating homogeneous synergetic conductive network of carbon black (CB) and multi-walled carbon nanotube (MWCNTs) composite on polydimethysiloxane (PDMS) sponge skeletons. The unique combination of a 3D porous structure, with hybrid conductive networks of CB/MWCNTs displayed a superior elasticity, with outstanding electrical characterization under external compression. The piezoresistive tactile sensor exhibited a high sensitivity of (15 kPa⁻¹), with a rapid response time (100 ms), the capability of detecting both large and small compressive strains, as well as excellent mechanical deformability and stability over 1000 cycles. Benefiting from a long-term stability, fast response, and low-detection limit, the piezoresistive sensor was successfully utilized in monitoring human physiological signals, including finger heart rate, pulses, knee bending, respiration, and finger grabbing motions during the process of picking up an object. Furthermore, a comprehensive performance of the sensor was carried out, and the sensor’s design fulfilled vital evaluation metrics, such as low-cost and simplicity in the fabrication process. Thus, 3D porous-based piezoresistive tactile sensors could rapidly promote the development of high-performance flexible sensors, and make them very attractive for an enormous range of potential applications in healthcare devices, wearable electronics, and intelligent robotic systems.

Success of the da Vinci surgical robot in the last decade has motivated the development of flexible access robots to assist clinical experts during single-port interventions of core intrabody organs. Prototypes of flexible robots have been proposed to enhance surgical tasks, such as suturing, tumor resection, and radiosurgery in human abdominal areas; nonetheless, precise constraint control models are still needed for flexible pathway navigation. In this paper, the design of a flexible snake-like robot is presented, along with the constraints model that was proposed for kinematics and dynamics control, motion trajectory planning, and obstacle avoidance during motion. Simulation of the robot and implementation of the proposed control models were done in Matlab. Several points on different circular paths were used for evaluation, and the results obtained show the model had a mean kinematic error of 0.37 ± 0.36 mm with very fast kinematics and dynamics resolution times. Furthermore, the robot’s movement was geometrically and parametrically continuous for three different trajectory cases on a circular pathway. In addition, procedures for dynamic constraint and obstacle collision detection were also proposed and validated. In the latter, a collision-avoidance scheme was kept optimal by keeping a safe distance between the robot’s links and obstacles in the workspace. Analyses of the results showed the control system was optimal in determining the necessary joint angles to reach a given target point, and motion profiles with a smooth trajectory was guaranteed, while collision with obstacles were detected a priori and avoided in close to real-time. Furthermore, the complexity and computational effort of the algorithmic models were negligibly small. Thus, the model can be used to enhance the real-time control of flexible robotic systems.

Interventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient’s vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons’ exposure to operational hazards including radiation. However, the absence of apt position control and force feedback remains a challenge. This study presents an isomorphic master–slave VISR for precise navigation of endovascular tools viz. catheters and guidewires. The master console aids operators in issuing manipulation commands and logs feedback from the force, rotation, and translation data. The slave manipulator uses the commands received from the master platform for actual tool navigation. However, precise master–slave position control and force feedback are precursors for optimal patient outcomes. This study utilized a fuzzy-PID controller for precise tool navigation and a neural network model for resistance force modulation with 50 mN precision. Furthermore, we evaluated the performance of using the learning-based models within our VISR and compared it with the performances from conventional methods. Results show that the models enhanced the proposed robotic system with better navigation precision, faster response speed, and improved force measurement capabilities.

Robot-assisted endovascular intervention has the potential to reduce radiation exposure to surgeons and enhance outcomes of interventions. However, the success and safety of endovascular interventions depend on surgeons’ ability to accurately manipulate endovascular tools such as guidewire and catheter and perceive their safety when cannulating patient's vessels. Currently, the existing interventional robots lack a haptic system for accurate force feedback that surgeons can rely on. In this paper, a haptic-enabled endovascular interventional robot was developed. We proposed a dynamic hysteresis compensation model to address the challenges of hysteresis and nonlinearity in magnetic powder brake-based haptic interface, which were used for providing high-precision and higher dynamic range haptic perception. Also, for the first time, a human perceptual-based haptic enhancement model and safety strategy were integrated with the custom-built haptic interface for enhancing sensation discrimination ability during robot-assisted endovascular interventions. This can effectively amplify even subtle changes in low-intensity operational forces such that surgeons can better discern any vessel-tools interaction force. Several experimental studies were performed to show that the haptic interface and the kinesthetic perception enhancement model can enhance the transparency of robot-assisted endovascular interventions, as well as promote the safety awareness of surgeon.