To address the challenges of low efficiency and poor robustness in multi-radiation-source term estimation within unknown environments, this paper proposes a variable state space-based particle filtering framework for multi-source term estimation. The framework dynamically constructs and updates the state space through the octree map integrated with radiation sensors. Efficient source term estimation is achieved via iterative observations and particle filtering algorithms. The global optimization capability of individual particles is enhanced through an adaptive differential evolution algorithm, while erroneous predictions are corrected by radiation distribution forecasting. Experimental validation involved Unmanned aerial vehicle (UAV) predictions and multi-algorithm comparisons across diverse scenarios, accompanied by systematic analysis of deviation sources. Results demonstrate that the proposed method effectively enables online inference of both radiation source parameters and their quantities from local multi-peak radiation fields.

This work proposes a bionic excitation strategy in which the impact force harvested by magnetic snap-through instability directly acts on the triboelectric substrate film, thereby significantly enhancing the power output of the bio-inspired energy harvester (BEH). Unlike traditional compression-driven TENGs, the impulsive stress loading shortens the single-pulse force response by approximately 50 ms, as confirmed by synchronized real-time time-voltage measurements. The rapid stress transition leads to a faster transient electrical response and contributes to increased charge output. As a result, the overall BEH achieves a 127% increase in total power output, a 161.5% increase in TENG power output, and a 126.5% increase in PET power output compared with traditional harvesters. These findings demonstrate that bionic abrupt-transition mechanisms can overcome the intrinsic limitations of low-frequency(<30hz) harvesters and unlock higher energy conversion efficiency. Furthermore, the BEH enables wireless temperature and humidity sensing, highlighting its potential for self-powered IoT applications. Overall, this work establishes a generalizable impulsive-excitation paradigm that breaks existing performance bottlenecks and advances the development of next-generation high-efficiency energy harvesting systems.

Imitation learning (IL) offers a promising pathway for enabling surgical robots to perform autonomous wound repair. However, existing methods often neglect spatial semantics and wound-shape information, leading to poor generalization and low success rates. This paper presents the Spatial-, Semantic-, and Shape-aware Diffusion Policy towards autonomous Wound Repair (S$^{3}$aDPWo), a framework integrating two visual perception modules tailored for wound repair: the Spatial Semantic Perception Module (SSPM) and the Wound Shape Perception Module (WSPM). These modules supply the action predictor with semantically enriched point clouds and keypoint-based wound geometric descriptors, enabling S$^{3}$aDPWo to jointly perceive spatial-semantic and wound-shape information. Experimental results demonstrate the effectiveness of the proposed algorithms in wound segmentation and keypoint prediction, and further validate the overall framework on wound approximation—a key contact-rich sub-task of wound repair essential for facilitating subsequent suturing and promoting healing. Notably, S$^{3}$aDPWo achieves success rates of 90% and 80% on seen and unseen wound instances, respectively, while maintaining mean errors below 3 mm across inter-edge distance, edge-height difference, and edge consistency. This substantially outperforms SOTA IL baselines in both generalization and performance.

BACKGROUND:MRI-guided neurosurgery requires high-precision puncture, but is challenged by magnetic field constraints and brain tissue deformation. METHODS:The mechanism is constructed from non-magnetic materials (e.g., PEEK and ceramic bearings) and driven by ultrasonic piezoelectric actuators to ensure safety in strong magnetic fields. A composite swing-arc RCM design extends the RCM workspace to a hemispherical region, enabling dynamic adjustment within a 220 mm diameter. D-H parameters are refined through multimodal calibration, and RCM stability is experimentally validated. RESULTS:After calibration, the end-effector absolute error is 2.16 mm with a repeatability of ± 1.02 mm, and the mean RCM deviation is 0.57 mm. CONCLUSIONS:The system supports autonomous puncture under real-time MRI, covers the cranial workspace and provides a precise, flexible solution for neurosurgical procedures.

Autonomous navigation has achieved significant success in structured indoor 2D environments but still encounters major challenges in unstructured outdoor 3D terrains, especially on uneven ground. To address these limitations, we propose a novel navigation framework that integrates continuous traversability estimation and safety-aware planning. The core innovation lies in three aspects: (1) a Bayesian generalized kernel inference method that estimates unobserved point cloud attributes to generate a continuous traversability map; (2) a traversability-aware A* algorithm (TRA-A*), which enhances global path planning by incorporating terrain constraints; and (3) a nonlinear model predictive control (NMPC) that integrates both traversability and uncertainty into the cost function to enable robust motion execution. These components collectively form a safety-focused navigation pipeline capable of stable and adaptive operation in complex terrains. Experimental results from simulations and real-world trials demonstrate that TRA-A* effectively enhances navigation safety and efficiency. Compared to the baseline A*, TRA-A* reduces terrain cost by 42.6% while incurring only a 9.83% increase in path length and a 10.7% increase in the number of nodes. Furthermore, when compared with the state-of-the-art PF-RRT*, it shortens the path length by 13.88%, reduces the number of nodes by 28.70%, and lowers terrain cost by 16.12%, demonstrating superior adaptability. In addition, NMPC significantly improves motion stability, reducing longitudinal acceleration fluctuations by 28.52% and lateral angular velocity fluctuations by 28.99%, thereby enabling more stable and reliable navigation.

BACKGROUND:Emerging imitation learning (IL) approaches have provided innovative solutions for completing surgical robotic suturing autonomously, significantly aiding surgeons in their manipulations. METHODS:We introduce Diffusion Policy for Autonomous Suturing (DP4AuSu), a novel framework that leverages diffusion policy (DP) and dynamic time wrapping-based locally weighted regression to achieve autonomous robotic suturing. RESULTS:In simulation, DP4AuSu achieved a 94% success rate for insertion subtasks over 50 trials. In a real-world setting, it achieves 85% success rate over 20 trials for suturing manipulations in 390.55-41.59s faster than conventional diffusion policy. CONCLUSIONS:Our novel framework can capture the multimodality in demonstrations and successfully learn the suturing policy and reduce the suturing time. To the best of our knowledge, this work represents the first application of diffusion policy for robotic suturing. We hope this research paves the way for the automation of more complex surgical tasks.

Brain puncture procedures generate complex lateral forces that, if excessive, can damage healthy tissue and deviate the needle from its intended path, increasing surgical risk. Traditional metallic force sensors are unsuitable for MRI environments due to their size and electromagnetic interference. To overcome these limitations, this study presents a fiber Bragg grating (FBG)-based lateral force sensing system integrated into a piezoelectric-driven brain puncture device, enabling real-time force monitoring under MRI conditions. Three FBG sensors are arranged at 120° intervals around the needle shaft, forming a compact, MRI-compatible sensing structure. A mechanical calibration model with temperature decoupling was established to ensure accurate measurements. Simulation and experimental results show a lateral force sensitivity of 990 pm/N in the 0–1 N range, with measurement error below 4.5% full scale (FS), and reliable operation in strong magnetic fields. This work offers a high-sensitivity, electromagnetically immune solution for force sensing in minimally invasive neurosurgery, providing technical support for safer and more precise MRI-guided brain puncture procedures.

To obtain accurate location information in dynamic environments, we propose a dynamic visual–inertial SLAM algorithm that can operate in real-time. In this paper, we combine the YOLO-V5 algorithm and the depth threshold extraction algorithm to achieve real-time pixel-level segmentation of objects. Meanwhile, to address the situation where dynamic targets are occluded by other objects, we design the object depth extraction method based on K-means clustering. We also design a factor graph optimization with rigid and non-rigid dynamic objects based on object category division, in order to better utilize the motion information of dynamic objects. We use the Kalman filter algorithm to achieve object matching and tracking. At the same time, to obtain as many rigid targets as possible, we design the adaptive rigid point set modeling algorithm to further supplement the rigid objects. Finally, we evaluate the algorithm through public datasets and self-built datasets, verifying its ability to handle dynamic environments.

To address the need for a puncture actuator with both magnetic resonance imaging (MRI) compatibility and high positioning accuracy in MRI-guided deep brain intervention, this paper introduces a novel two-degree-offreedom (2-DOF) asynchronous driven MRI-compatible stick-slip piezoelectric actuator with backward motion suppression. The proposed actuator features a 2-DOF piezoelectric-driven mechanism for precise needle feeding and rotation. An asynchronous dual-foot cooperative driving strategy enhances speed, load capacity, and stability, while an integrated flexure hinge effectively suppresses backward motion, improving accuracy and reliability. By alternately exciting the two driving feet, the actuator achieves continuous linear motion, optimizing the unidirectional puncture process for enhanced precision and safety. A prototype was developed and experimentally evaluated, achieving a maximum speed of 0.757 mm/s, an axial load capacity of 0.4 N, a unidirectional reciprocating error below 3.2 %, a maximum puncture force of 0.49 N, and a positioning resolution of 48 nm. Furthermore, a comparative analysis was performed to evaluate the driving characteristics of different MRIcompatible materials. The proposed actuator provides an effective MRI-compatible high-precision positioning solution for MRI-guided interventional procedures, with promising applications in neurosurgical puncture robots and targeted drug delivery systems.

Purpose Simultaneous localization and mapping (SLAM) is widely used in autonomous robotics. Although LiDAR and vision-based methods have been widely deployed in large-scale environments, their submeter accuracy is insufficient for operational scenarios. This paper aims to present a tightly integrated SLAM framework combining point clouds, visual landmarks and IMU data to enhance localization accuracy. Design/methodology/approach A novel method to resolve planar marker pose ambiguity is proposed. Edge constraints between the point cloud and marker correct marker’ pose and distortions. A tightly coupled coarse-to-fine optimization approach integrates point cloud features and marker corners during tracking and localization. Pose constraints are added to the objective function to improve accuracy, and bundle adjustment is applied to key frame and marker poses in the common view to prevent registration errors. Findings The proposed method is evaluated in real-world environments. Experimental results demonstrate that it achieves superior localization and mapping accuracy compared to existing methods, thus validating the effectiveness of the framework. Originality/value This paper introduces a tightly integrated framework that combines point clouds, visual landmarks and IMU measurements to improve small-scale localization accuracy in operational scenario.

We present an autonomous approaching scheme for mobile robot traversing obstacle stairwells, which overcomes the restricted field of vision caused by obstacles. The scheme consists of three parts: stair localization, structural parameter estimation, and nearing the stair. We use LIDAR with full-angle and long-distance measurement capabilities to initially determine the location of the stairs, and estimate staircase parameters using a depth camera with intensive data. Then, we propose an algorithm to optimize the location of stairs for real-time correction of target point locations during approaching. Finally, we conducted tests to verify the accuracy of the estimated stair positions and structural parameters and performed a comprehensive experiment to demonstrate the effectiveness of the scheme, in which the robot approaches autonomously to the front of the stairs around multiple obstacles.

To achieve high power output under low-intensity, broadband, time-varying vibrations, this paper introduces a Variable Stiffness Magnetic Oscillation (VSMO) energy harvesting device. The VSMO device comprises a cantilever beam and a variable stiffness magnetic oscillation element, with fixed magnets positioned at both ends of the magnetic oscillation element cavity and a floating magnet situated in the middle. Utilizing the kinetic energy generated by a magnet sliding in a cavity, this VSMO design achieves high power output. It functions autonomously across a broadband frequency range without requiring manual adjustments. The structural characteristics of the proposed VSMO were evaluated through numerical analysis, and its kinetic equations were constructed, and the core parameters were experimentally verified. Experimental data indicate that the VSMO can achieve a maximum power output of 23.7 mW at 18.7 Hz and 3.92 m/s², marking a 217.4% enhancement in power output compared to the original technology. The VSMO enables enhanced energy harvesting over a broadband frequency range of 1 Hz to 30.6 Hz. Furthermore, the device incorporates sensors for monitoring temperature and humidity in the vibrational surroundings, no lithium batteries, and employs Bluetooth for data transmission. This device holds promise for condition monitoring in unstable vibration environments, thus facilitating distributed monitoring within the realm of the Internet of Things.

In robot-assisted pelvic fracture reduction surgery, precise control of the traction needle is essential for successful traction of the fractured bone to align with the anastomotic position. However, needle deformation during the procedure can compromise the navigation system's accuracy, potentially causing over-traction, damage to adjacent tissues, and surgical failure. To address this issue, we propose a novel force sensing system that integrates fiber Bragg grating (FBG) sensors onto the traction needle, providing reliable mechanical feedback. The applied force on the needle is transduced through the wavelength shift of the FBGs, enabling the calculation of needle tip deformation using a mapping model correlating needle position offset and applied force. Experimental evaluation reveals the sensor's high sensitivity in measuring lateral force (31.04 and 21.91 pm/N in the ${X}$ and ${Y}$ directions, respectively) and commendable tracking accuracy for both force and deformation (with errors of 3.74%FS full scale (FS) and 1.280 mm). The proposed sensing system's efficacy is further demonstrated through robot-assisted pelvic reduction surgery on an artificial pelvis, confirming the sensor's capability to accurately measure traction force and instrument deformation during the procedure.

To achieve stable power output under low-intensity, broadband, time-varying vibrations, this paper proposes a Variable Stiffness Magnetic Oscillation (VSMO) energy harvester. The VSMO energy harvester is based on an integrated concept design, where the end magnet of the basic cantilever beam is replaced by a sliding magnetic element. Utilizing the kinetic energy generated by a magnet sliding in a cavity, this VSMO device achieves broadband energy harvesting without requiring manual adjustments. The working principle of the VSMO was analyzed through dynamic equations, and experiments were conducted to verify the effects of cantilever beam thickness, length, and sliding magnet mass on the output performance. Experimental data indicate that the VSMO can achieve a maximum power output of 47.4 mW, marking a 217.4 % enhancement in power output compared to the original technology. The minimum acceleration required for operation is only 0.05g. The VSMO enables enhanced output power over a broadband frequency range of 1 Hz-30.6 Hz. In addition, the device was able to power commercial sensors and use Bluetooth for data transmission. This device holds promise for condition monitoring in vibration environments, thus facilitating distributed monitoring within the realm of the Internet of Things.

With the advancement of robotics technology, industrial automation has become a trend in many scenarios. In fields such as warehousing logistics and service robotics, a reliable method for estimating the grasp pose of objects has become a necessary task. At present, many robotic grasping systems have achieved good results. However, due to the irregularity of the object surface and variation of illumination, the point cloud obtained by a single view has large holes and errors at the edge of the object. These errors easily lead to wrong estimations of grasp poses. To address these issues, this paper proposes a practical robot grasping method. The method is based on the idea of 6D pose estimation and point cloud fusion and complements the input point cloud with the model through 6D pose estimation. Then, the fused object point cloud is used to estimate the grasp pose through the Angle-View Net and fast search strategy. In order to ensure the accuracy of point cloud fusion, we use iterative closest point to correct the 6D pose. Finally, we conduct experiments in simulated and real environments, and the performance evaluation shows the feasibility of the method.

Traditional energy harvesters often have limitations in terms of bandwidth and power output, resulting in poor performance. This study reports a multi-strategy ultra-wideband energy harvesting device that utilizes an asymmetry, stagger array, magnetic coupling, and nonlinearity strategies to achieve high power output over an ultra-wideband frequency range without the need for external power input. The energy harvesting device consists of a base, two elastic beams, and two sets of asymmetric palm-shaped piezoelectric cantilever beam structures, each with a magnet attached at the tip. By reasonably arranging the palm-shaped mechanism and magnets, a magnetic coupling effect is introduced to achieve high power density output at non-resonant frequencies. Numerical analysis is conducted to evaluate the performance of the proposed structure, assess the influence of structural parameters, and establish a dynamic model to analyze the energy harvesting device. Experimental results demonstrate that the structure achieves a maximum power output of 60.49 mW at 19.9 Hz and 0.5 g, with a peak power density of approximately 8.065 x 103 W/m3. Within an ultra-wideband frequency range that spans 6 Hz to 64.2 Hz, the energy harvester maintains an output voltage which is more than 5 V. Furthermore, temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the energy harvesting device, eliminating the need for lithium batteries and ensuring stable signal transmission. The device can be utilized for condition monitoring in any unstable vibration environment, contributing to the realization of distributed monitoring in the Internet of Things (IoT).

The development of continuum robots with embedded sensing systems has been a research focus over the last decade. Specifically, for the robot-assisted laparo-endoscopic single-site surgical (R-LESS) system, an economical solution that allows the integration of an electromagnetic (EM)-compatible shape-proprioception system is needed. In this article, we propose a modular sensing system named X-Sketch for the R-LESS system. The system comprises four fiber Bragg grating (FBG) channels integrated with 3-D-printed elastic joints that enable the detection of curvature variations. The modularity and customizability are achieved by the stacking design of the flexible joints and sensing units. The skeleton of the arm is constructed by an analytical kinematics process of the curvature information, during which a neural network (NN) is employed to perform the curvature regression. For the experiment, an R-LESS arm prototype integrated with the X-Sketch system is developed. Calibration and experimental evaluation are then conducted to assess the performance of the proposed sensing system. The results demonstrate the performance of the proposed system, achieving an average tip position error of 2.23 +/- 0.64 mm, corresponding to <5% of the system's length, suggesting the potential for X-Sketch application in R-LESS surgical operations.

Pelvic fracture is a serious high-energy injury with the highest disability and mortality rate among all fractures. Therefore, the greater resetting force and more complex resetting path of the pelvic reduction robot also affect the accuracy of the navigation system. Type C pelvic fractures involve both rotational and vertical displacement, and the surgical instruments are likely to be obstructed during the surgical procedure, making it difficult for traditional optical localization methods in surgical navigation to meet the requirement of unobstructed visibility. Hence, in this article, a pelvic reduction localization and navigation method based on the fusion of electromagnetic and optical techniques is investigated to improve the positioning accuracy for achieving high resetting forces and complex resetting paths. This article utilizes an optical tracking system (OTS) and an electromagnetic tracking system (EMTS) to determine the spatial orientation of surgical instruments. The OTS offers high optical accuracy but requires a clear line of sight, while the EMTS provides lower electromagnetic accuracy and is subject to magnetic field distortions. A high-precision dynamic measurement method for internal and external poses during fracture reduction is proposed by integrating electromagnetic tracking and optical tracking technologies, to ensure continuous tracking of surgical instruments by the navigation system even when an optical line of sight is obstructed or magnetic field distortions occur. The sensors were integrated into the intelligent bone needle and calibrated accordingly first. Then, the error models of both electromagnetic and OTSs were analyzed. Based on the error analysis, we provided global error correction for the electromagnetic tracking by using the least-squares polynomial fitting method. The fusion of optical navigation and electromagnetic navigation was achieved through Kalman filtering, enabling robust tracking of surgical instruments. Our research findings demonstrate that the fusion positioning of the OTS and the EMTS effectively compensates for short marker occlusions and provides continuous estimation of the instrument's poses, thus meeting the requirements for real-time surgical navigation applications.