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学者姓名：卢宗兴
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Abstract ：
Miniature robots are increasingly used in unstructured environments and require higher mobility, robustness, and multifunctionality. However, existing purely soft and rigid designs suffer from inherent defects, such as low load capacity and compliance, respectively, restricting their functionality and performance. Here, we report new soft-rigid hybrid miniature robots applying the tensegrity principle, inspired by biological organisms' remarkable multifunctionality through tensegrity micro-structures. The miniature robot's speed of 25.07 body lengths per second is advanced among published miniature robots and tensegrity robots. The design versatility is demonstrated by constructing three bio-inspired robots using miniature tensegrity joints. Due to its internal load-transfer mechanisms, the robot has self-adaptability, deformability, and high impact resistance (withstand dynamic load 143,868 times the robot weight), enabling the robot to navigate diverse barriers, pipelines, and channels. The robot can vary its stiffness to greatly improve load capacity and motion performance. We further demonstrate the potential biomedical applications, such as drug delivery, impurity removal, and remote heating achieved by integrating metal into the robot.
Keyword ：
high-speed and adaptive locomotion high-speed and adaptive locomotion tunable stiffness tunable stiffness untethered miniature tensegrity robot untethered miniature tensegrity robot
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| GB/T 7714 | Chen, Bingxing , He, Zhiyu , Ye, Fang et al. Untethered Miniature Tensegrity Robot with Tunable Stiffness for High-Speed and Adaptive Locomotion [J]. | SOFT ROBOTICS , 2025 . |
| MLA | Chen, Bingxing et al. "Untethered Miniature Tensegrity Robot with Tunable Stiffness for High-Speed and Adaptive Locomotion" . | SOFT ROBOTICS (2025) . |
| APA | Chen, Bingxing , He, Zhiyu , Ye, Fang , Yang, Yi , Chen, Wenhu , Ding, Fuhui et al. Untethered Miniature Tensegrity Robot with Tunable Stiffness for High-Speed and Adaptive Locomotion . | SOFT ROBOTICS , 2025 . |
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Traditional quadruped robots are known for their agile movement and versatility across varied terrains. However, their foot structures struggle to navigate unstructured terrains such as pipes, slopes, and protrusions. This paper proposes a novel tensegrity foot structure consisting of a tensegrity ankle joint and an X-shaped adaptive tensegrity footpad, which enhances the terrain adaptability of legged robots. The equilibrium equation of the ankle joint is established, and the relationship between the translational stiffness of the ankle joint and the spring stiffness is derived. Additionally, a mathematical model for the number of X-shaped tensegrity footpad units and their relationship with the deformation height and length of the tensegrity footpad is established. A physical prototype of the tensegrity foot was fabricated using 3D printing. Experiments are conducted to validate the adaptability of both the ankle joint and the tensegrity footpad. The results indicate that the proposed adaptive tensegrity foot structure exhibits good adaptability on unstructured terrains with varying radii, slopes, steps, S-curves, and spherical surfaces. The tensegrity foot structure can enhance the environmental adaptability of quadruped robots and has excellent impact resistance effects.
Keyword ：
adaptive locomotion adaptive locomotion ankle joint ankle joint quadruped robot quadruped robot shock absorption shock absorption tensegrity tensegrity
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| GB/T 7714 | Dong, Hui , Gan, Jiahao , Xia, Rongbiao et al. Adaptive tensegrity foot design for quadruped robots in unstructured terrains [J]. | SMART MATERIALS AND STRUCTURES , 2025 , 34 (2) . |
| MLA | Dong, Hui et al. "Adaptive tensegrity foot design for quadruped robots in unstructured terrains" . | SMART MATERIALS AND STRUCTURES 34 . 2 (2025) . |
| APA | Dong, Hui , Gan, Jiahao , Xia, Rongbiao , Lu, Zongxing , Chen, Bingxing , Chen, Muhao . Adaptive tensegrity foot design for quadruped robots in unstructured terrains . | SMART MATERIALS AND STRUCTURES , 2025 , 34 (2) . |
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The wheeled bipedal robots have great application potential in environments with a mixture of structured and unstructured terrain. However, wheeled bipedal robots have problems such as poor balance ability and low movement level on rough roads. In this paper, a novel and low-cost wheeled bipedal robot with an asymmetrical five-link mechanism is proposed, and the kinematics of the legs and the dynamics of the Wheeled Inverted Pendulum (WIP) are modeled. The primary balance controller of the wheeled bipedal robot is built based on the Linear Quadratic Regulator (LQR) and the compensation method of the virtual pitch angle adjusting the Center of Mass (CoM) position, then the whole-body hybrid torque-position control is established by combining attitude and leg controllers. The stability of the robot's attitude control and motion is verified with simulations and prototype experiments, which confirm the robot's ability to pass through complex terrain and resist external interference. The feasibility and reliability of the proposed control model are verified.
Keyword ：
Wheeled Robots Legged Robots Motion Control Mechanism Design Wheeled Robots Legged Robots Motion Control Mechanism Design
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| GB/T 7714 | Xiong, Yi , Liu, Haojie , Chen, Bingxing et al. Whole-Body Hybrid Torque-Position Control for Balancing with a New Wheeled Bipedal Robot [J]. | JOURNAL OF BIONIC ENGINEERING , 2025 , 22 (2) : 626-641 . |
| MLA | Xiong, Yi et al. "Whole-Body Hybrid Torque-Position Control for Balancing with a New Wheeled Bipedal Robot" . | JOURNAL OF BIONIC ENGINEERING 22 . 2 (2025) : 626-641 . |
| APA | Xiong, Yi , Liu, Haojie , Chen, Bingxing , Chen, Yanjie , Yao, Ligang , Lu, Zongxing . Whole-Body Hybrid Torque-Position Control for Balancing with a New Wheeled Bipedal Robot . | JOURNAL OF BIONIC ENGINEERING , 2025 , 22 (2) , 626-641 . |
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Aims: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients. Methods: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP). Results: The SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set. Conclusion: The proposed ML-based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment. © 2024 British Pharmacological Society.
Keyword ：
covariate model covariate model machine learning machine learning periprosthetic joint infection periprosthetic joint infection SHAP SHAP vancomycin trough concentration vancomycin trough concentration
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| GB/T 7714 | Chen, Y.-W. , Lin, X.-K. , Huang, C. et al. Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model [J]. | British Journal of Clinical Pharmacology , 2024 , 90 (9) : 2188-2199 . |
| MLA | Chen, Y.-W. et al. "Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model" . | British Journal of Clinical Pharmacology 90 . 9 (2024) : 2188-2199 . |
| APA | Chen, Y.-W. , Lin, X.-K. , Huang, C. , Wu, W. , Lin, W.-W. , Chen, S. et al. Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model . | British Journal of Clinical Pharmacology , 2024 , 90 (9) , 2188-2199 . |
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The balance control of the wheeled bipedal robots has been relatively mature, but the jump control of the wheeled bipedal robots is not perfect at present. Aiming at problems such as significant landing impact and less than expected jump height of the wheeled bipedal robots, a method of jump trajectory planning with specific soft landing ability is proposed. The dynamic model of the robot jumping and the basis for distinguishing the land and fly phases are established. The planning of the wheel and foot trajectories and the main body trajectories during the robot jumping is completed, and the tracking of the robot jumping trajectories is completed using virtual model control and virtual force compensation. Through simulations and prototype experiments, the robot can perform various jumping tasks with high precision jump heights while still being able to land without much impact. The stability and effectiveness of the proposed method are demonstrated.
Keyword ：
Force Force Jump control Jump control Legged locomotion Legged locomotion Robot kinematics Robot kinematics Robots Robots Trajectory Trajectory trajectory planning trajectory planning Trajectory planning Trajectory planning wheeled bipedal robot wheeled bipedal robot Wheels Wheels
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| GB/T 7714 | Lu, Zongxing , Xiong, Yi , Liu, Haojie et al. Trajectory Planning for Jumping and Soft Landing With a New Wheeled Bipedal Robot [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (11) : 13406-13415 . |
| MLA | Lu, Zongxing et al. "Trajectory Planning for Jumping and Soft Landing With a New Wheeled Bipedal Robot" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 11 (2024) : 13406-13415 . |
| APA | Lu, Zongxing , Xiong, Yi , Liu, Haojie , Yao, Ligang , Wang, Zhiyong . Trajectory Planning for Jumping and Soft Landing With a New Wheeled Bipedal Robot . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (11) , 13406-13415 . |
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Due to the current focus of research on ankle rehabilitation robots on structural design, there is still limited research on ankle human-machine interaction technology. In order to enable rehabilitation robots to conduct personalized rehabilitation training based on patients' ankle movement intentions, we propose a new ankle motion recognition method based on plantar pressure. First, we designed a plantar pressure collection system based on array sensors. Then, we collected nine types of ankle joint motion pressure data from five volunteers and conducted algorithm selection, data processing, and algorithm optimization. Finally, we proposed a small sample optimization algorithm based on support vector machine, with an average recognition rate of 93.16%. The recognition method proposed in this paper can be combined with ankle rehabilitation robots to achieve active rehabilitation functions, laying the foundation for the clinical application of active rehabilitation technology.
Keyword ：
acquisition system acquisition system algorithm optimization algorithm optimization data processing data processing motion recognition motion recognition Plantar pressure Plantar pressure
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| GB/T 7714 | Lu, Zongxing , Xu, Zhiwei , Zhao, Dongzhe et al. ANKLE JOINT MOTION RECOGNITION SYSTEM AND ALGORITHM OPTIMIZATION BASED ON PLANTAR PRESSURE [J]. | JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY , 2024 , 24 (10) . |
| MLA | Lu, Zongxing et al. "ANKLE JOINT MOTION RECOGNITION SYSTEM AND ALGORITHM OPTIMIZATION BASED ON PLANTAR PRESSURE" . | JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 24 . 10 (2024) . |
| APA | Lu, Zongxing , Xu, Zhiwei , Zhao, Dongzhe , Yang, Tianxue . ANKLE JOINT MOTION RECOGNITION SYSTEM AND ALGORITHM OPTIMIZATION BASED ON PLANTAR PRESSURE . | JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY , 2024 , 24 (10) . |
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AimsAlthough there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients.MethodsThe dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP).ResultsThe SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set.ConclusionThe proposed ML-based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment.
Keyword ：
covariate model covariate model machine learning machine learning periprosthetic joint infection periprosthetic joint infection SHAP SHAP vancomycin trough concentration vancomycin trough concentration
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| GB/T 7714 | Chen, Yue-Wen , Lin, Xi-Kai , Huang, Chen et al. Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model [J]. | BRITISH JOURNAL OF CLINICAL PHARMACOLOGY , 2024 , 90 (9) : 2188-2199 . |
| MLA | Chen, Yue-Wen et al. "Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model" . | BRITISH JOURNAL OF CLINICAL PHARMACOLOGY 90 . 9 (2024) : 2188-2199 . |
| APA | Chen, Yue-Wen , Lin, Xi-Kai , Huang, Chen , Wu, Wei , Lin, Wei-Wei , Chen, Si et al. Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model . | BRITISH JOURNAL OF CLINICAL PHARMACOLOGY , 2024 , 90 (9) , 2188-2199 . |
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The hand gesture recognition (HGR) technology in A-mode ultrasound human-machine interface (HMI-A), based on traditional machine learning, relies on intricate feature reduction methods. Researchers need prior knowledge and multiple validations to achieve the optimal combination of features and machine learning algorithms. Furthermore, anatomical differences in the forearm muscles among different subjects prevent specific subject models from applying to unknown subjects, necessitating repetitive retraining. This increases users' time costs and limits the real-world application of HMI-A. Hence, this article presents a lightweight 1-D four-branch squeeze-to-excitation convolutional neural network (CNN) (4-branch SENet) that outperforms traditional machine learning methods in both feature extraction and gesture classification. Building upon this, a weight fine-tuning strategy using transfer learning enables rapid gesture recognition across subjects and time. Comparative analysis indicates that the freeze feature and fine-tuning fully connected (FC) layers result in an average accuracy of 96.35% +/- 3.04% and an average runtime of 4.8 +/- 0.15 s, making it 52.9% faster than subject-specific models. This method further extends the application scenarios of HMI-A in fields such as medical rehabilitation and intelligent prosthetics.
Keyword ：
A-mode ultrasound A-mode ultrasound convolutional neural network (CNN) convolutional neural network (CNN) Convolutional neural networks Convolutional neural networks deep learning deep learning Feature extraction Feature extraction Gesture recognition Gesture recognition hand gesture recognition (HGR) hand gesture recognition (HGR) human-machine interaction (HMI) human-machine interaction (HMI) Muscles Muscles Sensors Sensors transfer learning transfer learning Transfer learning Transfer learning Ultrasonic imaging Ultrasonic imaging
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| GB/T 7714 | Lian, Yue , Lu, Zongxing , Huang, Xin et al. A Transfer Learning Strategy for Cross-Subject and Cross-Time Hand Gesture Recognition Based on A-Mode Ultrasound [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (10) : 17183-17192 . |
| MLA | Lian, Yue et al. "A Transfer Learning Strategy for Cross-Subject and Cross-Time Hand Gesture Recognition Based on A-Mode Ultrasound" . | IEEE SENSORS JOURNAL 24 . 10 (2024) : 17183-17192 . |
| APA | Lian, Yue , Lu, Zongxing , Huang, Xin , Shangguan, Qican , Yao, Ligang , Huang, Jie et al. A Transfer Learning Strategy for Cross-Subject and Cross-Time Hand Gesture Recognition Based on A-Mode Ultrasound . | IEEE SENSORS JOURNAL , 2024 , 24 (10) , 17183-17192 . |
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The development of intelligent rehabilitation robots has greatly reduced the workload of rehabilitation physicians. Human-machine interaction (HMI) control methods are a critical technology for intelligent rehabilitation robots. Therefore, we systematically review the HMI methods and control strategies for upper and lower limb rehabilitation robots and summarizing the HMI methods with different sensors. The integration of rehabilitation robots and HMI control methods has grown significantly in recent years. For this reason, this article takes the sensing methods as the entry point to give readers a quick overview of the current status of HMI research. We present different sensing methods, interactive control strategies, applications, and evaluation methods and discuss the limitations and future development directions in the field. The results show that the mainstream control methods of HMI are based on motion signals, surface electromyography (sEMG), ultrasound (US), and electroencephalogram (EEG). In the field of rehabilitation robotics, human intention recognition-based interaction strategy is the mainstream HMI strategy, which mainly collects biosignals, force/moment, spatial angle, and other information for human intention recognition. Future research may focus on the use of multimodal sensing interactions, flexible control strategies, and generalized rehabilitation assessment mechanism.
Keyword ：
Control strategies Control strategies human intention recognition human intention recognition human-machine interaction (HMI) human-machine interaction (HMI) rehabilitation robot rehabilitation robot sensing methods sensing methods
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| GB/T 7714 | Lu Zongxing , Zhang Jie , Yao Ligang et al. The HumanMachine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (9) : 13773-13787 . |
| MLA | Lu Zongxing et al. "The HumanMachine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review" . | IEEE SENSORS JOURNAL 24 . 9 (2024) : 13773-13787 . |
| APA | Lu Zongxing , Zhang Jie , Yao Ligang , Chen Jinshui , Luo Hongbin . The HumanMachine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review . | IEEE SENSORS JOURNAL , 2024 , 24 (9) , 13773-13787 . |
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Tensegrity robotic fish is an emerging solution in the field of robotic fish, and its dynamic model plays a crucial role in guiding the improvement of swimming performance. However, there is a lack of in-depth research in this area. This paper proposes a planar dynamic model for the tensegrity robotic fish, which can be used to simulate and investigate the influence of body stiffness on swimming performance. An equivalent modeling approach is introduced for the soft fish skin and tail fin to consider their effects of flexibility on the robotic fish's swimming motion. A hydrodynamic discretization model based on virtual nodes is proposed to incorporate hydrodynamic effects such as drag force, lift force, and added mass into the fish's vertebral column. By integrating the above models, the dynamic model for the tensegrity robotic fish is established after embedding the tail joint constraints. The method of reconstructing fish body waves in the simulation is provided to calculate fish swimming characteristics. The parameter identification method for the drag coefficient and torque coefficient is proposed. The dynamic model of the tensegrity robotic fish is verified using swimming experimental data. By conducting numerous numerical simulations, the effects of the body stiffness distribution, tail fin stiffness, and fish skin stiffness on the swimming performance are analyzed. The simulation results reveal the significant role of adjusting body stiffness in enhancing the swimming characteristics of the robotic fish.
Keyword ：
Dynamics Dynamics Hydrodynamic Hydrodynamic Soft skin and tail Soft skin and tail Tensegrity robotic fish Tensegrity robotic fish
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| GB/T 7714 | Chen, Bingxing , Zhang, Jie , Meng, Qiuxu et al. A dynamic model of tensegrity robotic fish considering soft fish skin and tail [J]. | NONLINEAR DYNAMICS , 2024 , 113 (1) : 329-353 . |
| MLA | Chen, Bingxing et al. "A dynamic model of tensegrity robotic fish considering soft fish skin and tail" . | NONLINEAR DYNAMICS 113 . 1 (2024) : 329-353 . |
| APA | Chen, Bingxing , Zhang, Jie , Meng, Qiuxu , Lu, Zongxing , Zhao, Chong , Jiang, Hongzhou . A dynamic model of tensegrity robotic fish considering soft fish skin and tail . | NONLINEAR DYNAMICS , 2024 , 113 (1) , 329-353 . |
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