Precise cortical and cancellous bone segmentation is essential for safe laminectomy. However, it remains challenging due to limited annotations and high anatomical similarity. To mitigate these challenges, we propose MambaMatch, an end-to-end semi-supervised segmentation framework collaboratively optimized under a teacher–student paradigm. The student branch adopts an innovative Mamba-ASPP-Unet (MAU) module, which integrates multi-scale Atrous Spatial Pyramid Pooling (ASPP) with channel and spatial attention to actively extract global spinal structures and cortical boundary features, while the teacher branch guides the student through constraints imposed by the KL-divergence. To enhance feature diversity, a dual-stream perturbation strategy is employed, combining Correlation-Guided CutMix Augmentation (CGCA) on high-response regions with standard strong augmentations. Furthermore, to account for inter-class variations in pseudo-label reliability, dynamic thresholding and temperature scaling are further introduced to adaptively balance pseudo-label selection and the intensity of consistency loss. MambaMatch achieves an mIoU of 79.69% and a Dice of 83.57% on our clinically validated CT Cortical– Cancellous Dataset, and also shows strong robustness on MRI-SPIDER, SKIN-ISIC 2018, PH2, and CT-VerSe datasets and the model remains lightweight and efficient. These results show that MambaMatch provides an efficient and accurate segmentation framework with clear potential to support clinical workflows in spinal surgery, while also demonstrating value across a broader range of clinical imaging scenarios.

Natural Orifice Transluminal Endoscopic Surgery (NOTES) holds great promise due to its ability to eliminate external incisions, reduce trauma, and accelerate recovery. However, the adoption of NOTES is hindered by the limited capabilities of existing instruments, particularly in achieving the required balance between compact size, dexterity, and load capacity. This paper introduces a novel robotic manipulator designed for NOTES, featuring a 5 mm diameter and 7 degrees of freedom (DoF). The manipulator incorporates an innovative 3-PRS flexible parallel mechanism combined with a continuum parallel structure, achieving enhanced dexterity and variable stiffness functionality within a miniaturized design. A kinematic and variable stiffness analysis is performed, and experimental validation demonstrates its bending performance and stiffness modulation. Additionally, the feasibility and practicality of the robotic system are confirmed through a peg-transfer experiment, proving its potential for real-world surgical applications. This research offers a viable solution for enhancing the performance of NOTES instruments.

The parallel continuum mechanism offers distinct advantages in the design of surgical manipulators, including enhanced stiffness, improved precision, and a simplified structure compared to traditional Tendon-driven systems. Conventional kinematic models based on constant-curvature assumptions are often inadequate for accurately capturing the complex bending behaviors of this mechanism. In contrast, the Cosserat rod theory provides a rigorous framework for precise kinematic modeling of flexible structures. However, its computational complexity results in slow solving speeds, particularly when dealing with spatial points that are widely separated. This paper focuses on a miniaturized parallel continuum manipulator and employs the Cosserat rod model for kinematic modeling, combined with a neural network-based inverse kinematics solver to achieve rapid real-time computation. To expedite inverse kinematics, a multilayer perceptron is trained on 5,000 samples generated from the Cosserat rod model, yielding the average absolute error of 0.046mm and the average relative error of 0.41% in predicting rod lengths. Experimental validation demonstrates that the neural network solver reduces computation time to about 0.16ms compared to 700–3100ms for conventional numerical methods, underscoring its potential for enhancing the precision and responsiveness of surgical systems in minimally invasive procedures.

This work proposed surgical forceps as a sensor that integrated step-coated Fiber Bragg Grating (FBG) for the 3-axial force sensing in the percutaneous spinal endoscopic robot. The step-coated FBG achieved a reflection spectrum with double wavelength peaks, providing more wavelength signals for fault-tolerant decoupling. The temperature and force sensitivity were regulated by adjusting the coating size to reduce decoupling error to 3.68% F.S, solving the span temperature (≥ 20 °C) disturbance from the operating room to the operative area. The optical fiber metallization and laser welding package was proposed to achieve stable connections between the quartz optical fiber and metal forceps. The innovative package made the forceps tolerate 180 °C dry-heat sterilization and aqueous erosion, allowing for sterilized reuse (11 times with error lower than 4.98% F.S) and long-term stability (one month with error lower than 4.36% F.S). A Wavelet Fuzzy Entropy (WFE) and Extreme Learning Machine (ELM) based dynamic fault-tolerant decoupling strategy was proposed. The WFE-ELM reduced the sensor error to 4.42% F.S. under the influence of spectrum chirp noise and single-branch FBG breakage, and the fault-tolerant recovery rate within 10% F.S. error was raised to 40.23%. The designed surgical instruments were integrated with a spinal endoscopic surgical robot to conduct the spine model and pig experiments, verifying its force-sensing effectiveness.

Endoscopy holds a pivotal role in the early detection and treatment of diverse diseases, with artificial intelligence (AI)-assisted methods increasingly gaining prominence in disease screening. Among them, the depth estimation from endoscopic sequences is crucial for a spectrum of AI-assisted surgical techniques. However, the development of endoscopic depth estimation algorithms presents a formidable challenge due to the unique environmental intricacies and constraints within the dataset. This paper proposes a self-supervised depth estimation network to comprehensively explore the brightness changes in endoscopic images, and fuse different features at multiple levels to achieve an accurate prediction of endoscopic depth. First, a FlowNet is designed to evaluate the brightness changes of adjacent frames by calculating the multi-scale structural similarity. Second, a feature fusion module is presented to capture multi-scale contextual information. Experiments show that the average accuracy of the algorithm is 97.03% in the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED dataset). Based on the training parameters of the SCARED dataset, the algorithm achieves superior performance on the other two datasets (EndoSLAM and KVASIR dataset), indicating that the algorithm has good generalization performance.

This paper proposes a novel, more computationally efficient method for optimizing robot excitation trajectories for dynamic parameter identification, emphasizing self-collision avoidance. This addresses the system identification challenges for getting high-quality training data associated with co-manipulated robotic arms that can be equipped with a variety of tools, a common scenario in industrial but also clinical and research contexts. Utilizing the Unified Robotics Description Format (URDF) to implement a symbolic Python implementation of the Recursive Newton-Euler Algorithm (RNEA), the approach aids in dynamically estimating parameters such as inertia using regression analyses on data from real robots. The excitation trajectory was evaluated and achieved on par criteria when compared to state-of-the-art reported results which didn’t consider self-collision and tool calibrations. Furthermore, physical Human-Robot Interaction (pHRI) admittance control experiments were conducted in a surgical context to evaluate the derived inverse dynamics model showing a 30.1% workload reduction by the NASA TLX questionnaire.

In mandibular angle split osteotomy (MASO), prominent mandibular angles need to be cut off with saws such as reciprocating saws. Compared to traditional-freehand methods, robot-assisted methods provide potentials for better cutting performance. In the robot-assisted mandibular angle split osteotomy (RAMASO), a cutting method based on shared control is proposed along with an optimization-planned osteotomy curve. Experimental verification using planes and skull phantoms were conducted and discussed for evaluation of accuracy and safety. The results in 7 cutting experiments for the following error were mainly within 0.76mm and -1.00mm (Q3±1.5*IQR), peaking at 1.80 mm. The maximum of time-consuming was 304.0s, with the average human robot interactive force reaching around 3.3 N. Experiments indicate the proposed method achieves better performance in accuracy and efficiency compared with the free hand. Note to Practitioners—This paper is inspired by the curve-cutting osteotomy task under the combination of pre-defined virtual fixtures and the kinematic constraint of the reciprocating saw. The motions of current surgical osteotomy robots are mostly generated by either of virtual fixtures and kinematic constraints, which is representing less autonomy on surgery. The introduction of autonomy in surgical robotics can greatly increase the surgeon’s performance in efficiency, accuracy, and safety. The technique of shared control is capable of achieving the semi-autonomy task to significantly improve the accuracy with feedback mechanisms in control science. Thus, the advanced control strategies are required into the process of surgical osteotomy operation. In this article, we proposed a novel methodology to control the hands-on robot executing a curve path. The developed control scheme has the following functionalities: 1) it enables the lateral control for the cutting task for the hands-on robot system. 2) it maintains the pre-defined path-generated virtual fixtures and finds the optimal-parameters of the virtual fixtures. For the convenience of presentation, the mandibular angle split osteotomy is chosen as the background, but in fact this method can be extended to more surgical and even industrial applications with a similar scenario.

Imitation learning is pivotal for robot skill acquisition and holds potential for teleoperation shared control applications. This article introduces a novel imitation learning method and shared control framework using unit tangent fuzzy movement primitives (UTFMP). Using unsupervised demonstration segmentation results obtained by UTFMP as membership function references, and employing type-2 fuzzy models to manage uncertainties in segmented subtrajectories across multiple demonstrations, we propose an imitation learning method. This method facilitates trajectory learning based on either time or distance, supports real-time trajectory modulation using inputs, such as time, position, or velocity, adeptly adapts to dynamic scenarios, and possesses the capability to seamlessly blends diverse trajectories. Then, a shared control framework with segmented subtrajectories and modulated trajectories is established to extend UTFMP's application. This framework includes intent recognition through fuzzy fusion and a Markov transition matrix to identify intent of operator, as well as shared arbitration to integrate operations from both operator and robot agent. Simulation experiments validate UTFMP's effectiveness for demonstration segmentation, imitation learning and modulation. Teleoperation experiments demonstrate increased trajectory smoothness and accuracy by at least 24.38% and 9.48%, respectively, a minimum 16.56% reduction in control time compared to conventional velocity control using the shared control proposed.

The reactor power and the coolant level in the nuclear plant are monitored via the reactor core detectors. Every 4 to 5 years, the detectors with high-level radiation need to be removed, which is time-consuming and hazardous for workers. To address this issue, this paper introduces a novel robotic system and its strategy for the removal of the detectors. The modular mechanisms are designed to achieve diverse actions such as positioning, extracting, transporting, cutting, and coiling. The detector with different radiation doses is physically classified and minimized in volume. The experiments to simulate the removal process are conducted. The results demonstrate that the time for the robotic removal of one detector is reduced from more than 1 hour to 31.2±5.3 min compared with the manual mode. The radiation exposure time for workers is reduced to 0 under normal working conditions, which significantly reduces the radiation dose compared with the traditional methods.

Multi-Stud Tensioning Machine (MSTM) is a specialized equipment used to open/seal the cover of the Reactor Pressure Vessel (RPV) during nuclear power plant maintenance. The tensioning residual values of the 58 studs are monitored for procedure evaluation. It is time-consuming for human operators to place the measurement meters into working positions. In order to reduce labor intensity and eliminate radiation exposure time, we develop a robot-assisted automatic measurement system to achieve meter placement and real-time data monitoring. The Field Programmable Gate Array (FPGA)-based distributed control scheme realizes high-speed data acquisition and coordinated control of the 58 node robots. The control software performs data analysis and sends emergency stop signals to the MSTM control PLC. The proposed system is validated in China Nuclear Power Technology Research Institute. Total operation time decreases from over 580 s to less than 120 s.

The paper describes the kinematic modeling of a small wheel-track-legged mobile robot MOBIT which is targeted to applications in various hazardous environments to carry out military and civilian missions. The kinematic modeling for MOBIT has some individuality because of its multi-locomotion modes named wheeled, tracked, legged mode and obstacle negotiating mode. Different kinematics equations are derived based on its multi-locomotion modes respectively, and their features of kinematics in different modes are revealed. Particularly, the kinematic description of differential driven in wheeled mode, characteristics of slippage in tracked mode and posture definition in legged mode are presented mathematically. Unlike other robot operating on a flat surface, the proposed equations are solved to obtain the robot position and posture which can be used for autonomous motion control of robot operating in rough terrains

Although some researchers have studied the humanoid running based on simplified models, the issue of humanoid running based on the whole dynamics has not been sufficiently discussed before. The objective of this paper is to study the stability criterion and the dynamic pattern generation for humanoid running based on the whole dynamics. First, the cycle and the dynamics of running are analyzed. Next, the stability criterion of humanoid running is presented. Then, the method to plan a running pattern consisting of a foot trajectory and a hip trajectory is proposed. Finally, the effectiveness of the proposed method is illustrated by the dynamic simulation examples in DADS (Dynamic Analysis and Design System).