To steer a soft robot precisely in an unconstructed environment with minimal collision remains an open challenge for soft robots. When the environments are unknown, prior motion planning for navigation may not always be available. This article presents a novel Sim-to-Real method to guide a cable-driven soft robot in a static environment under the Simulation Open Framework Architecture (SOFA). The scenario aims to resemble one of the steps during a simplified transoral tracheal intubation process where a robotic endotracheal tube is guided to the upper trachea–larynx location by a flexible video-assisted endoscope/stylet. In SOFA, we employ the quadratic programming inverse solver to obtain collision-free motion strategies for the endoscope/stylet manipulation based on the robot model and encode the virtual eye-in-hand vision. Then, we associate the anatomical features recognized by the virtual vision and the joint space motion using a closed-loop nonlinear autoregressive exogenous model (NARX) network. Afterward, we transfer the learned knowledge to the robot prototype, expecting it to navigate to the desired spot in a new phantom environment automatically based on its eye-in-hand vision only. Experiment results indicate that our soft robot can efficaciously navigate through the unstructured phantom to the desired spot with minimal collision motion according to what it has learned from the virtual environment. The results show that the average R-squared coefficient between the closed-loop NARX-forecasted and SOFA-referenced robot's cable and prismatic joint space motion are 0.963 and 0.997, respectively. The eye-in-hand visions also demonstrate a good alignment between the robot tip and the glottis.

Magnetic soft robots have attracted widespread attention for their untethered, remotely operated, and compliant deformation characteristics. Earlier work has demonstrated magnetic origami robots' diverse locomotion capabilities. This letter will focus on the motion generation and open-loop control of an untethered magnetic flexible robot with a stretch-twist coupling origami spring (OS) skeleton only using uniform magnetic field control. We investigate the associated motion generation mechanism and the corresponding control signals for the magnetic spring robot (MSR). The MSR can perform in-plane crawling ( Worm Crawling ) and perpendicular in-plane crawling ( Crab Crawling ) under two-dimensional magnetic signal inputs. Moreover, the OS's stretch-twist coupling characteristic is utilized to achieve axial Rolling Motion with axial magnetization configuration. We further experimentally tested the performance of three motions with average normalized velocities of 0.34 $\pm$ 0.039(body length/s), 0.054 $\pm$ 0.0066(body length/s), and 1.46 $\pm$ 0.069(body length/s), respectively.

This paper presents a novel dynamic tactile sensor with a symmetrical longitudinal piezoelectric cantilever structure, which is different from the conventional transverse cantilever structure. It has a radical size of 1.1 centimeters. A flexible hinge chain and mass probe are introduced into the structure to reduce the sensor’s natural frequency to improve the estimation accuracy of tissue hardness. Double piezoelectric patches, symmetrically distributed in the sensor, are used as the actuator to generate vibration and as the sensor for resonance detection. The sensor’s basic working principle is derived mathematically, followed by simulation verification of statics analysis for safety consideration, anisotropic vibration mode analysis, harmonic response analysis, and a calibration experiment to establish fitting curves. The threshold point is set as 192 Hz/0.6 MPa in the calibration curve, and the hardness range is divided into a soft range (0–0.6 MPa) with the sensitivity of 9.75 Hz/MPa, which could realize hardness measurement of soft tissue and normal tissue, and a hard range (which exceeds 0.6 MPa) with higher frequency to realize the identification of the lesion tissue. Finally, the sensor prototype is tested on biological tissue (fresh pig liver) to verify that the calibration curve with the selected threshold is effective for identifying lesion areas and that the sensor prototype has a function of hardness measurement with a relatively stable sensitivity and lesion identification in its operating frequency range.

With the emerging need for human–machine interactions, multi-modal sensory interaction is gradually pursued rather than satisfying common perception forms (visual or auditory), so developing flexible, adaptive, and stiffness-variable force-sensing devices is the key to further promoting human–machine fusion. However, current sensor sensitivity is fixed and nonadjustable after fabrication, limiting further development. To solve this problem, we propose an origami-inspired structure to achieve multiple degrees of freedom (DoFs) motions with variable stiffness for force-sensing, which combines the ductility and flexibility of origami structures. In combination with the pneumatic actuation, the structure can achieve and adapt the compression, pitch, roll, diagonal, and array motions (five motion modes), which significantly increase the force adaptability and sensing diversity. To achieve closed-loop control and avoid excessive gas injection, the ultra-flexible microfiber sensor is designed and seamlessly embedded with an approximately linear sensitivity of ∼0.35 $\mathrm{\Omega }$/kPa at a relative pressure of 0–100 kPa, and an exponential sensitivity at a relative pressure of 100–350 kPa, which can render this device capable of working under various conditions. The final calibration experiment demonstrates that the pre-pressure value can affect the sensor’s sensitivity. With the increasing pre-pressure of 65–95 kPa, the average sensitivity curve shifts rightwards around 9 N intervals, which highly increases the force-sensing capability towards the range of 0–2 N. When the pre-pressure is at the relatively extreme air pressure of 100 kPa, the force sensitivity value is around 11.6 $\mathrm{\Omega }$/N. Therefore, our proposed design (which has a low fabrication cost, high integration level, and a suitable sensing range) shows great potential for applications in flexible force-sensing development.

Tracheotomy is commonly performed for patients needing prolonged intubation, airway obstruction, and neck injuries. Accurate placement of the incision and the tracheal window is paramount in order to avoid complications. Current surgical technique heavily relies on palpating cartilage landmarks on the neck to place the incision. In order to achieve the accelerated goals of the robot-assisted subtask in a tracheotomy, this paper proposes a novel autonomous palpation-based acquisition strategy -RASEC in the tracheal region, which can interactively predict the next acquisition point to maximize the expected information and minimize the costs of palpation procedure. We employ a Gaussian Process (GP) to model the distribution of hardness and utilize anatomical information as a priori input to guide the point of palpation for medical robots. The dynamic tactile sensor based on the resonant frequency is introduced to measure tissue hardness in the tracheal region by millimeter-scale contact to secure the interaction. We investigate the kernel fusion method to blend the Squared Exponential (SE) kernel with the Ornstein-Uhlenbeck (OU) kernel and optimize the Bayesian optimization search by leveraging the anatomical information of the larynx as a priori knowledge. Moreover, we further regularize the exploration and greed factors. The tactile sensor’s moving distance and the robotic base link’s rotation angle during the incision localization process are considered new factors in the acquisition strategy. Simulation and physical phantom experiments are conducted for comparison with state-of-the-art GP-based exploration approaches. The results show that the sensor’s moving distance was reduced by 53.1 $\%$ and the rotation angle of the base was reduced by 75.2 $\%$ of the previous values without sacrificing overall performance capabilities. The satisfying algorithmic index (average precision 0.932, average recall 0.973, average F1 score 0.952) with fewer central estimation distance errors (0.423 mm) and high resolution (1 mm) indicates the performance of the proposed RASEC in terms of exploration efficiency, cost awareness, and localization accuracy for incision localization and recommendation in real robot-assisted subtask in the tracheotomy procedure. Note to Practitioners —This work is well motivated to introduce the Level of Autonomy (LoA) 2 -task-level autonomy, specifically in the context of tracheotomy procedures. The incorporation of robotic palpation techniques aims to provide surgeons with enhanced capabilities for incision recommendations, which directly benefit surgeons to visualize hands-on information and localize the trachea regions more efficiently and further reduce cognitive load. To detect the trachea region for intubation incision without costly ergodic acquisition, this article suggests a highly efficient acquisition strategy utilizing the fusion kernel function and regularized impact factors, eliminating the time consumption for such localization task. The actual clinical value is that our proposed strategy can earn more time for further increasing the probability of patient resuscitation, to facilitate supervised autonomy in the real clinic scene.