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Robustness analysis and prediction of topological edge states in topological elastic waveguides

PHYSICA SCRIPTA 2024年第7期99卷

作者： Tong, Shiqi Sun, Wenjun Xu, Jiawen Li, Huijun Southeast Univ Sch Instrument Sci & Engn Jiangsu Key Lab Remote Measurement & Control Nanjing Jiangsu Peoples R China

Topological elastic waveguides constructed using acoustic topological insulators have garnered significant attention due to their exceptional wave modulation properties. While the existence of these edge states is guaranteed by topology, their robustness to defects is unclear. In this paper, topological edge states based on the acoustic pseudo-spin Hall effect are constructed, and the robustness of the topological edge states is quantitatively studied by analyzing displacement fields of phononic crystal (PnC) plates with various defects. Our robustness assessment considers nearly all possible defect scenarios, focusing on the influence of defects on three primary indicators: transmittance, maximum displacement and its specific location on the PnC plate. The results indicate that the topological edge states formed by this structure are highly robust to defects with varying rotation angles, but exhibit limited robustness to defects of different dimensions or positions. Furthermore, a Light Gradient Boosting Machine (LightGBM) model is employed to predict the displacement along the wave transmission path in the presence of diverse lattice defects. The model emerges as an accurate predictor of displacement distribution changes, and thus can provide potential optimization strategies for topologically elastic waveguide-based energy harvesting systems and self-powered sensors.

关键词： phononic crystals topological edge state topological elastic waveguides machine learning

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Lorentz attractor excitation-based structural damage identification using state space curvature reconstruction- enhanced transformer

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SMART MATERIALS AND STRUCTURES 2024年第11期33卷 115005-115005页

作者： Wang, Xian You, Zengying Pu, Xiaoqing Shao, Nan Xu, Jiawen Southeast Univ Sch Instrument Sci & Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Jiangsu Peoples R China

Vibration-based structural damage identification has been widely investigated. Different from previous studies that analyze vibrational responses in time and frequency domains, a new Lorentz attractor excitation-based damage identification is becoming a novel strategy with the advantage of capturing the structure's nonlinear dynamic effects. In this study, Lorentz attractor-based chaotic signals were employed as excitation signals for the structural damage identification of a frame structure. Nonlinear responses were recorded and damages of bolt looseness at different locations were considered. The structural damages could be revealed in the state-space plot of the responses. A state space curvature reconstruction method was introduced to enhance the key features of the nonlinear responses. A small-sample damage identification is performed using a deep learning algorithm-a transformer with an accuracy of 92.38%. The advantages of the proposed method over conventional deep learning algorithms were validated. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses.

关键词： Lorentz attractor structural damage identification transformer small sample

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Chattering suppression and hydrodynamic disturbance estimation of underwater manipulators using adaptive fuzzy sliding mode control

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TRANSACTIONS OF THE INSTITUTE OF measurement AND control 2024年第1期46卷 155-166页

作者： Zhang, Mingquan Song, Guangming Mao, Juzheng Wang, Fei Zhou, Jun Song, Aiguo Southeast Univ Sch Instrument Sci & Engn State Key Lab Digital Med Engn Jiangsu Key Lab Remote Measurement & Control Nanjing Peoples R China Southeast Univ Sch Instrument Sci & Engn State Key Lab Digital Med Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China

Robotic manipulators are nowadays widely used in various underwater scenarios, but their motion control remains a challenging task due to hydrodynamic effects. This article proposes a novel adaptive fuzzy sliding mode control (AFSMC) strategy for precise and robust control of underwater manipulators. To simulate the movement of the robotic manipulators in the underwater environment, the Unified Robot Description Format (URDF) file of a custom-designed electric underwater manipulator is imported into the Simscape Multibody and the hydrodynamic disturbance is modeled according to Morison's equation. Furthermore, the proposed control strategy takes advantage of the universal approximation capability of fuzzy systems to avoid chattering and observe disturbances by adjusting the control gains of classical sliding mode control (CSMC). And adaptive laws are designed to update the parameters of the fuzzy systems. The strong friction caused by the seal is also compensated by actual test data. In the simulation experiments, a special environment of water flow and variable loads is considered. The results demonstrate that the AFSMC strategy can achieve high precision and strong robustness against disturbances for trajectory tracking. More importantly, the chattering caused by CSMC can be eliminated and the hydrodynamic disturbance can be estimated with high precision through the proposed control strategy.

关键词： Adaptive fuzzy sliding mode control disturbance estimation trajectory tracking underwater manipulator multibody simulation

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Design and Characterization of a Novel Compact Hand Exoskeleton Robot for Telerehabilitation and Muscle Spasticity Assessment

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第4期29卷 2416-2427页

作者： Lai, Jianwei Song, Aiguo Southeast Univ Sch Instrument Sci & Engn State Key Lab Bioelect Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China

Rehabilitation robots can aid patients in performing hand exercises in their own home. However, existing rehabilitation equipment is bulky and difficult to wear and carry, and therapists are unable to remotely assess a patient's finger muscle spasticity. This article describes a lightweight exoskeleton robot that facilitates hand rehabilitation exercises and enables muscle spasticity assessment at home. A hand exoskeleton with one degree of freedom assists patients in flexion and extension movements of their fingers. Its motor has a reduction ratio of 19:1, allowing passive back-driving. The exoskeleton's link lengths are determined by an optimization algorithm. The proposed device has a total weight of 0.356 kg and the torque of the dynamic structure to the metacarpophalangeal joint is 1.832 N m, reflecting its lightweight and portable nature. To aid remote assessments of patients' muscle spasticity, the exoskeleton is controlled using a finger tension feedback algorithm. This enables the patient's rehabilitation process to be managed remotely. Experiments involving ten patients and three therapists are conducted to evaluate the robot's feasibility. The results demonstrate that the robot can flex and extend the fingers with a mean angle error of 1.16 degrees and a mean contact force error of 0.25 N. Moreover, the robot achieves 75% accuracy in assisting therapists with remote assessment of the patient's finger muscle spasticity.

关键词： Rehabilitation robot underactuation wearable robot

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A LiDAR SLAM Based on Clustering Features and Constraint Separation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND measurement 2025年 74卷

作者： Wang, Shaohu Song, Aiguo Miao, Tianyuan Ji, Qinjie Li, Huijun Southeast Univ Sch Instrument Sci & Engn State Key Lab Digital Med Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China

Simultaneous localization and mapping (SLAM) is a key technology for robot localization. Currently, most robot application scenarios are limited to either indoor or outdoor environments. When robots operate across both indoor and outdoor environments, they often experience significant positioning errors or even failures. To address this problem, we propose a light detection and ranging (LiDAR) SLAM approach based on clustering features and constraint separation. First, the ground is removed from the point cloud, followed by rough segmentation of the remaining point cloud. Feature points are then extracted from each rough cluster using normalized principal component analysis (PCA) and reflection intensity information, enabling fine segmentation. Clusters are separated based on their eigenvectors, and pose variables are estimated independently according to the different constraints of the separated clusters in various directions. These estimates are then weighted and fused to complete the front-end odometry. In the back-end optimization, scan and local map matching, physical world constraints, and loop closure are employed to jointly optimize pose variables, resulting in a globally consistent point cloud map. We evaluated our method on public and custom datasets, and the results show that, compared to other state-of-the-art methods, our approach achieves better localization accuracy and more robust performance in both indoor and outdoor environments, as well as in combined indoor-outdoor scenarios.

关键词： Constraint separation indoor and outdoor envi- ronments light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) point cloud segmentation (PCS) point cloud segmentation (PCS) point cloud segmentation (PCS)

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UTC-RS: An Underwater Tracked Cleaning Robot System for Hydraulic Structures

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2311-2318页

作者： Mao, Juzheng Zhou, Jun Xie, Feng Liu, Yue Song, Guangming Song, Aiguo Southeast Univ Sch Instrument Sci & Engn State Key Lab Digital Med Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China

During the inspection and maintenance of the underwater part of hydraulic structures, it is often necessary to clean the surface of a certain area for subsequent operations. At present, there are still few robots capable of underwater fine cleaning. Therefore, this letter introduces the design of a novel tracked robot system that can be used for underwater fine surface cleaning operations. The robot uses a crawling track chassis and is equipped with a self-designed high-precision operating robotic arm, force-controlled grippers, and underwater brushing tools. Considering the complexity of the underwater environment, an underwater force-position hybrid control algorithm suitable for the robot is proposed, taking into account the water flow resistance. Its effectiveness has been verified in the experimental pool and the proposed robot system has been applied in the actual engineering site. The robot achieves a brushing efficiency of approximately 72 m(2) per hour, with a force control accuracy of +/- 0.5 N, and the cleaning effect shows a significant improvement when compared to the condition before cleaning.

关键词： Cleaning Robots Hydraulic systems Surface cleaning Mobile robots Force control Brushes Grippers Autonomous underwater vehicles Service robots Concrete surface cleaning force-position hybrid control underwater tracked robot

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Experimental analysis of hybrid multiple-layer piezoelectric-solar energy harvester for self-powered wireless sensing

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2025年 73卷

作者： Yang, Hongxin Shao, Nan Wang, Jingxiong Xu, Jiawen Xu, Husheng Zhang, Yuanjing Southeast Univ Sch Instrument Sci & Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Jiangsu Peoples R China Jiangsu Police Inst Nanjing 210031 Jiangsu Peoples R China

In this study, a hybrid multiple-layer piezoelectric-solar energy harvester is proposed, which can be used to realize self-powered low-power wireless sensing in different environments. A new multiple-layer piezoelectric energy harvester was explored for vibration energy harvesting and tested with integrated solar cells. The vibration energy harvester was tested in three cases with different nonlinearity. The solar energy harvester and the vibration energy harvester were then combined to harvest ambient energy. The harvested solar energy and vibration energy were then stored in a capacitor. A low-power wireless sensing node was designed and composed of an Attiny85 microcontroller, temperature and humidity sensor, and wireless communication module. The power supply algorithm was developed for a regular evaluation of the energy in the storage capacitor and the command of operation of wireless sensing. Experimental results showed that the harvester can support the operation of the wireless sensor in hybrid, standalone vibration, and solar energy modes, respectively. The proposed hybrid energy harvesting and low-power wireless sensing node can be applied in remote sensing in power transmission lines, buildings, and coastline monitoring.

关键词： Hybrid energy harvesting Solar energy Piezoelectric vibration energy Low power consumption Self-powered wireless sensing node

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Interaction-Based Active Perception Method and Vibration-Audio-Visual Information Fusion for Asteroid Surface Material Identification

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IEEE TRANSACTIONS ON INSTRUMENTATION AND measurement 2024年 73卷 1页

作者： Zhang, Jun Xiao, Yi Ding, Yizhuang Chen, Liuchen Song, Aiguo Southeast Univ Sch Instrument Sci & Engn State Key Lab Digital Med Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China

Asteroid exploration is challenging due to its unknown surface material and microgravity. This article proposes an interaction-based active perception (IBAP) framework and a vibration-audio-visual information fusion (VAVIF) method for asteroid surface material identification. First, a robotic sensor node was designed to acquire vibration and audio signals when impacting the surface. With the help of the "interaction" between the node and surface material, a camera records visual images of the material splashing. Then, we proposed a SimpleCNN (SCNN) model to explore the rich features of the fusion images converted from the vibration signals for material identification. We also proposed a bidirectional RNN with attention mechanism (AB-RNN) model to fuse the high- and low-frequency vibration signals to improve the recognition performance. Results showed that SCNN and AB-RNN have better performance than the state-of-the-art models. In addition, state-of-the-art machine learning models are used to classify the visual images and audio signals for identification. Moreover, we utilized the output of multiple classifiers to construct a new dataset and employed ensemble learning (EL) for training and testing. The EL-based VAVIF obtained a recognition accuracy of 99.7%, higher than any individual learner. The results indicate that IBAP and VAVIF make material identification more accurate and robust, which can improve the success rate of an asteroid exploration mission.

关键词： Robot sensing systems Solar system Robots Visualization Aircraft manufacture Vibrations Cameras Active perception asteroid exploration ensemble learning (EL) information fusion material identification

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Multi-View Registration of Partially Overlapping Point Clouds for Robotic Manipulation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第10期9卷 8451-8458页

作者： Xie, Yuzhen Song, Aiguo Southeast Univ Sch Instrument Sci & Engn State Key Lab Digital Med Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Peoples R China

Point cloud registration is a fundamental task in intelligent robots, aiming to achieve globally consistent geometric structures and providing data support for robotic manipulation. Due to the limited view of measurement devices, it is necessary to collect point clouds from multiple views to construct a complete model. Previous multi-view registration methods rely on sufficient overlap and registering all pairs of point clouds, resulting in slow convergence and high cumulative errors. To solve these challenges, we present a multi-view registration method based on the point-to-plane model and pose graph. We introduce a robust kernel into the objective function to diminish registration errors caused by mismatched points. Additionally, an enhanced Euclidean clustering method is proposed for extracting object point clouds. Subsequently, by establishing pose constraints on non-adjacent frames of point clouds, the cumulative error is reduced, achieving global optimization based on the pose graph. Experimental results demonstrate the robustness of our method with respect to overlap ratios, successfully registering point clouds with overlap ratio exceeding 30$%. In comparison to other techniques, our method can reduce the E (R) of multi-view registration by 13.54$% and E (t) by 18.72$%, effectively reducing the cumulative error.

关键词： Point cloud registration multi-view registration robust kernel function pose graph optimization Point cloud registration multi-view registration robust kernel function pose graph optimization

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Zero-shot domain adaptation with enhanced consistency for semantic segmentation

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Yang, Jiming Da, Feipeng Hong, Ru Cai, Zeyu Gai, Shaoyan Key Lab Measurement & Control Complex Syst Engn Nanjing 210000 Jiangsu Peoples R China

Zero-shot domain adaptation is a specialized area within transfer learning focused on achieving domain adaptation without using any samples from the target domain. This is particularly important when target domain samples are difficult to obtain. The rapid development in generative models, particularly diffusion models, has introduced robust tools for zero-shot domain adaptation tasks. This paper proposes an innovative framework to address domain adaptive semantic segmentation under zero-shot conditions. We introduce a Dynamic control Fusion Module, which autonomously learns the fusion scales and effectively integrates hidden states with image controls, enhancing generation in complex scenarios. Furthermore, we propose a Semantic and Image-Text Consistency Strategy, designed to impose consistency constraints on both the semantic content and the style of generated images, ensuring closer alignment with the target domain. We perform experiments on Cityscapes, ACDC, and GTAV datasets. The results show that our method improves the quality of generated target domain images and semantic segmentation performance, demonstrating its effectiveness in zero-shot domain adaptation tasks. Overall, our method shows consistent improvements over baseline approaches across the five sub-experiments. Overall, our method demonstrates consistent improvements over baseline approaches across most domain adaptation tasks. Specifically, in the tasks involving adaptation to Night and Snow, it achieves 2.6% and 2.3% higher mIoU compared to the baseline, respectively.

关键词： Domain adaptation Zero-shot Diffusion models

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