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学者姓名：邓震
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Abstract ：
柔性内窥镜机器人具有连续体结构,在微创手术领域展现出独特优势,但连续体结构的非线性变形特征导致柔性内窥镜机器人的运动控制精度不足.针对上述问题,本文提出一种基于神经动力学的柔性内窥镜机器人最优遥操作控制方法.首先,通过构建图像空间下内窥镜机器人主从运动映射机制,建立柔性内窥镜运动学模型,获得图像特征速度与驱动速度的映射关系;其次,基因关节运动约束将机器人运动控制转化为二次规划最优控制问题,并使用基于神经动力学的实时求解器进行高效求解;最后,在输尿管镜机器人平台开展实验验证.实验结果表明:本文方法可有效减小人工操作误差和速度振荡,目标点跟踪误差被控制在2.5%以内,同时有效提升了碎石术中器械操控的准确性和稳定性.
Keyword ：
主从遥操作 主从遥操作 最优控制 最优控制 柔性内窥镜机器人 柔性内窥镜机器人 神经动力学优化 神经动力学优化
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| GB/T 7714 | 张杰阳 , 何帅 , 邓震 et al. 基于神经动力学优化的柔性内窥镜机器人最优遥操作控制 [J]. | 集成技术 , 2025 , 14 (2) : 3-12 . |
| MLA | 张杰阳 et al. "基于神经动力学优化的柔性内窥镜机器人最优遥操作控制" . | 集成技术 14 . 2 (2025) : 3-12 . |
| APA | 张杰阳 , 何帅 , 邓震 , 何炳蔚 . 基于神经动力学优化的柔性内窥镜机器人最优遥操作控制 . | 集成技术 , 2025 , 14 (2) , 3-12 . |
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In this letter, a constrained visual predictive control strategy (C-VPC) is developed for a robotic flexible endoscope to precisely track target features in narrow environments while adhering to visibility and joint limit constraints. The visibility constraint, crucial for keeping the target feature within the camera's field of view, is explicitly designed using zeroing control barrier functions to uphold the forward invariance of a visible set. To automate the robotic endoscope, kinematic modeling for image-based visual servoing is conducted, resulting in a state-space model that facilitates the prediction of the future evolution of the endoscopic state. The C-VPC method calculates the optimal control input by optimizing the model-based predictions of the future state under visibility and joint limit constraints. Both simulation and experimental results demonstrate the effectiveness of the proposed method in achieving autonomous target tracking and addressing the visibility constraint simultaneously. The proposed method achieved a reduction of 12.3% in Mean Absolute Error (MAE) and 56.0% in variance (VA) compared to classic IBVS.
Keyword ：
Bending Bending Cameras Cameras Endoscopes Endoscopes Flexible robotics Flexible robotics image-based visual servoing image-based visual servoing Jacobian matrices Jacobian matrices Predictive control Predictive control robotic flexible endoscope robotic flexible endoscope Robot kinematics Robot kinematics Robots Robots Target tracking Target tracking Visualization Visualization visual predictive control visual predictive control Visual servoing Visual servoing
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| GB/T 7714 | Deng, Zhen , Liu, Weiwei , Li, Guotao et al. Constrained Visual Predictive Control of a Robotic Flexible Endoscope With Visibility and Joint Limits Constraints [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2025 , 10 (2) : 1513-1520 . |
| MLA | Deng, Zhen et al. "Constrained Visual Predictive Control of a Robotic Flexible Endoscope With Visibility and Joint Limits Constraints" . | IEEE ROBOTICS AND AUTOMATION LETTERS 10 . 2 (2025) : 1513-1520 . |
| APA | Deng, Zhen , Liu, Weiwei , Li, Guotao , Zhang, Jianwei . Constrained Visual Predictive Control of a Robotic Flexible Endoscope With Visibility and Joint Limits Constraints . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2025 , 10 (2) , 1513-1520 . |
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Tendon-driven continuum robots (TDCRs) have infinite degrees of freedom and high flexibility, posing challenges for accurate modeling and autonomous control, especially in confined environments. This paper presents a model-less optimal visual control (MLOVC) method using neurodynamic optimization to enable autonomous target tracking of TDCRs in confined environments. The TDCR's kinematics are estimated online from sensory data, establishing a connection between the actuator input and visual features. An optimal visual servoing method based on quadratic programming (QP) is developed to ensure precise target tracking without violating the robot's physical constraints. An inverse-free recurrent neural network (RNN)-based neurodynamic optimization method is designed to solve the complex QP problem. Comparative simulations and experiments demonstrate that the proposed method outperforms existing methods in target tracking accuracy and computational efficiency. The RNN-based controller successfully achieves target tracking within constraints in confined environments. © 2024 Elsevier B.V.
Keyword ：
Neurodynamic optimization Neurodynamic optimization Optimal visual control Optimal visual control Robotic ureteroscopy Robotic ureteroscopy Tendon-driven continuum robots Tendon-driven continuum robots Visual servoing Visual servoing
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| GB/T 7714 | He, S. , Zou, C. , Deng, Z. et al. Model-less optimal visual control of tendon-driven continuum robots using recurrent neural network-based neurodynamic optimization [J]. | Robotics and Autonomous Systems , 2024 , 182 . |
| MLA | He, S. et al. "Model-less optimal visual control of tendon-driven continuum robots using recurrent neural network-based neurodynamic optimization" . | Robotics and Autonomous Systems 182 (2024) . |
| APA | He, S. , Zou, C. , Deng, Z. , Liu, W. , He, B. , Zhang, J. . Model-less optimal visual control of tendon-driven continuum robots using recurrent neural network-based neurodynamic optimization . | Robotics and Autonomous Systems , 2024 , 182 . |
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Accurate control of continuum robots in confined environments presents a significant challenge due to the need for a precise kinematic model, which is susceptible to external interference. This paper introduces a model-less optimal visual control (MLOVC) method that enables a tendon-sheath-driven continuum robot (TSDCR) to effectively track visual targets in a confined environment while ensuring stability. The method allows for intraluminal navigation of TSDCRs along narrow lumens. To account for the presence of external outliers, a robust Jacobian estimation method is proposed, wherein improved iterative reweighted least squares with sliding windows are used to online calculate the robot's Jacobian matrix from sensing data. The estimated Jacobian establishes the motion relationship between the visual feature and the actuation. Furthermore, an optimal visual control method based on quadratic programming (QP) is designed for visual target tracking, while considering the robot's physical constraint and control constraints. The MLOVC method for visual tracking provides a reliable alternative that does not rely on the precise kinematics of TSDCRs and takes into consideration the impact of outliers. The control stability of the proposed approach is demonstrated through Lyapunov analysis. Simulations and experiments are conducted to evaluate the effectiveness of the MLOVC method, and the results demonstrate that it enhances tracking performance in terms of accuracy and stability. © 2024 Elsevier Ltd
Keyword ：
Jacobian matrices Jacobian matrices Machine vision Machine vision Quadratic programming Quadratic programming Robot programming Robot programming Robot vision Robot vision Visual servoing Visual servoing
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| GB/T 7714 | Pan, Chuanchuan , Deng, Zhen , Zeng, Chao et al. Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments [J]. | Mechatronics , 2024 , 104 . |
| MLA | Pan, Chuanchuan et al. "Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments" . | Mechatronics 104 (2024) . |
| APA | Pan, Chuanchuan , Deng, Zhen , Zeng, Chao , He, Bingwei , Zhang, Jianwei . Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments . | Mechatronics , 2024 , 104 . |
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The ability to effectively classify human emotion states is critically important for human-computer or human-robot interactions. However, emotion classification with physiological signals is still a challenging problem due to the diversity of emotion expression and the characteristic differences in different modal signals. A novel learning-based network architecture is presented that can exploit four-modal physiological signals, electrocardiogram, electrodermal activity, electromyography, and blood volume pulse, and make a classification of emotion states. It features two kinds of attention modules, feature-level, and semantic-level, which drive the network to focus on the information-rich features by mimicking the human attention mechanism. The feature-level attention module encodes the rich information of each physiological signal. While the semantic-level attention module captures the semantic dependencies among modals. The performance of the designed network is evaluated with the open-source Wearable Stress and Affect Detection dataset. The developed emotion classification system achieves an accuracy of 83.88%. Results demonstrated that the proposed network could effectively process four-modal physiological signals and achieve high accuracy of emotion classification. © 2024 The Author(s). Cognitive Computation and Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Shenzhen University.
Keyword ：
affective computing affective computing neural net architecture neural net architecture neural nets neural nets
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| GB/T 7714 | Zou, C. , Deng, Z. , He, B. et al. Emotion classification with multi-modal physiological signals using multi-attention-based neural network [J]. | Cognitive Computation and Systems , 2024 , 6 (1-3) : 1-11 . |
| MLA | Zou, C. et al. "Emotion classification with multi-modal physiological signals using multi-attention-based neural network" . | Cognitive Computation and Systems 6 . 1-3 (2024) : 1-11 . |
| APA | Zou, C. , Deng, Z. , He, B. , Yan, M. , Wu, J. , Zhu, Z. . Emotion classification with multi-modal physiological signals using multi-attention-based neural network . | Cognitive Computation and Systems , 2024 , 6 (1-3) , 1-11 . |
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The ability to automatically segment anatomical targets on medical images is crucial for clinical diagnosis and interventional therapy. However, supervised learning methods often require a large number of pixel-wise labels that are difficult to obtain. This paper proposes a weakly supervised glottis segmentation (WSGS) method for training end-to-end neural networks using only point annotations as training labels. This method functions by iteratively generating pseudo-labels and training the segmentation network. An automatic seeded region growing (ASRG) algorithm is introduced to generate quality pseudo labels to diffuse point annotations based on network prediction and image features. Additionally, a novel loss function based on the structural similarity index measure (SSIM) is designed to enhance boundary segmentation. Using the trained network as its core, a glottis state monitor is developed to detect the motion behavior of the glottis and assist the anesthesiologist. Finally, the performance of the proposed approach was evaluated on two datasets, achieving an average mIoU and accuracy of 82.7% and 91.3%. The proposed monitor was demonstrated to be effective, which holds significance in clinical applications.
Keyword ：
Glottis segmentation Glottis segmentation Medical image segmentation Medical image segmentation Weakly supervised learning Weakly supervised learning
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| GB/T 7714 | Wei, Xiaoxiao , Deng, Zhen , Zheng, Xiaochun et al. Weakly supervised glottis segmentation on endoscopic images with point supervision [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 92 . |
| MLA | Wei, Xiaoxiao et al. "Weakly supervised glottis segmentation on endoscopic images with point supervision" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 92 (2024) . |
| APA | Wei, Xiaoxiao , Deng, Zhen , Zheng, Xiaochun , He, Bingwei , Hu, Ying . Weakly supervised glottis segmentation on endoscopic images with point supervision . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 92 . |
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Contact-rich manipulation tasks are difficult to program to be performed by robots. Traditional compliance control methods, such as impedance control, rely excessively on environmental models and are ineffective in the face of increasingly complex contact tasks. Reinforcement learning (RL) has now achieved great success in the fields of games and robotics. Autonomous learning of manipulation skills can empower robots with autonomous decision-making capabilities. To this end, this work introduces a novel learning framework that combines deep RL (DRL) and variable impedance control (VIC) to achieve robotic massage tasks. A skill policy is learned in joint space, which outputs the desired impedance gain and angle for each joint. To address the limitations of the sparse reward of DRL, an intrinsic curiosity module (ICM) was designed, which generates the intrinsic reward to encourage robots to explore more effectively. Simulation and real experiments were performed to verify the effectiveness of the proposed method. Our experiments demonstrate that contact-rich massage skills can be learned through the VIC-DRL framework based on the joint space in a simulation environment, and that the ICM can improve learning efficiency and overall performance in the task. Moreover, the generated policies have been demonstrated to still perform effectively on a real-world robot.
Keyword ：
Aerospace electronics Aerospace electronics Decision making Decision making Deep learning Deep learning Games Games Impedance Impedance Reinforcement learning Reinforcement learning Task analysis Task analysis
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| GB/T 7714 | Li, Zhuoran , Zeng, Chao , Deng, Zhen et al. Learning Variable Impedance Control for Robotic Massage With Deep Reinforcement Learning: A Novel Learning Framework [J]. | IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE , 2024 , 10 (1) : 17-27 . |
| MLA | Li, Zhuoran et al. "Learning Variable Impedance Control for Robotic Massage With Deep Reinforcement Learning: A Novel Learning Framework" . | IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE 10 . 1 (2024) : 17-27 . |
| APA | Li, Zhuoran , Zeng, Chao , Deng, Zhen , Xu, Qinling , He, Bingwei , Zhang, Jianwei . Learning Variable Impedance Control for Robotic Massage With Deep Reinforcement Learning: A Novel Learning Framework . | IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE , 2024 , 10 (1) , 17-27 . |
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Tendon-driven continuum robots (TDCRs) with mechanical compliance have gained popularity in natural orifice transluminal endoscopic surgery (NOTES). Teleoperation problems of the TDCRs involve performance objectives in addition to the visibility constraint. Handling the coupling between potentially conflicting objectives and the visibility constraint remains challenging for surgeons when operating TDCRs. This paper presents a shared control method to assist in the teleoperation of the TDCRs, which guarantees visual targets remain within the field of view (FoV) of the TDCR. The visibility constraint is explicitly defined using a zeroing control barrier function, which is specified in terms of the forward invariance of a visible set. To ensure accuracy, the Jacobian matrix of the system is approximated online using sensing data. Then, the visibility constraint, along with the robot's physical constraints, is integrated into a quadratic program (QP) framework. This allows for the optimization of the control input of the operator subject to constraints, thus preserving visibility. Finally, simulations and experiments were conducted to demonstrate the effectiveness of the proposed approach under two teleoperation modes. The results show that the proposed method achieved a reduction of approximately 70% in ITP and 43% in MAE compared to direct teleoperation.
Keyword ：
continuum robot continuum robot control barrier function control barrier function optimal visual control optimal visual control Robotic endoscopic surgery Robotic endoscopic surgery
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| GB/T 7714 | Deng, Zhen , Wei, Xiaoxiao , Pan, Chuanchuan et al. Shared Control of Tendon-Driven Continuum Robots Using Visibility-Guaranteed Optimization for Endoscopic Surgery [J]. | IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS , 2024 , 6 (2) : 487-497 . |
| MLA | Deng, Zhen et al. "Shared Control of Tendon-Driven Continuum Robots Using Visibility-Guaranteed Optimization for Endoscopic Surgery" . | IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 6 . 2 (2024) : 487-497 . |
| APA | Deng, Zhen , Wei, Xiaoxiao , Pan, Chuanchuan , Li, Guotao , Hu, Ying . Shared Control of Tendon-Driven Continuum Robots Using Visibility-Guaranteed Optimization for Endoscopic Surgery . | IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS , 2024 , 6 (2) , 487-497 . |
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Autonomous robotic massage holds the potential to alleviate the workload of nurses and improve the quality of healthcare. However, the complexity of the task and the dynamic of the environment present significant challenges for robotic massage. This paper presents a vision-based robotic massage (VBRM) framework that facilitates autonomous robot massaging of the human body while ensuring safe operation in a dynamic environment. The VBRM framework allows the operator to define the massage trajectory by drawing a 2D curve on an RGB image. An interactive trajectory planning method is developed to calculate a 3D massage trajectory from the 2D trajectory. This method accounts for potential movements of the human body and updates the planned trajectory using rigid point cloud registration. Additionally, a hybrid motion/force controller is employed to regulate the motion of the robot's end-effector, considering the possibility of excessive contact force. The proposed framework enables the operator to adjust the massage trajectory and speed according to their requirements. Real-world experiments are conducted to evaluate the efficacy of the proposed approach. The results demonstrate that the framework enables successful planning and execution of the massage task in a dynamic environment. Furthermore, the operator has the flexibility to set the massage trajectory, speed, and contact force arbitrarily, thereby enhancing human-machine interaction.
Keyword ：
Interactive trajectory planning Interactive trajectory planning Physical robot-environment interaction Physical robot-environment interaction Robot massage Robot massage Visual servoing Visual servoing
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| GB/T 7714 | Xu, Qinling , Deng, Zhen , Zeng, Chao et al. Toward automatic robotic massage based on interactive trajectory planning and control [J]. | COMPLEX & INTELLIGENT SYSTEMS , 2024 , 10 (3) : 4397-4407 . |
| MLA | Xu, Qinling et al. "Toward automatic robotic massage based on interactive trajectory planning and control" . | COMPLEX & INTELLIGENT SYSTEMS 10 . 3 (2024) : 4397-4407 . |
| APA | Xu, Qinling , Deng, Zhen , Zeng, Chao , Li, Zhuoran , He, Bingwei , Zhang, Jianwei . Toward automatic robotic massage based on interactive trajectory planning and control . | COMPLEX & INTELLIGENT SYSTEMS , 2024 , 10 (3) , 4397-4407 . |
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Accurate control of continuum robots in confined environments presents a significant challenge due to the need for a precise kinematic model, which is susceptible to external interference. This paper introduces a model- less optimal visual control (MLOVC) method that enables a tendon-sheath-driven continuum robot (TSDCR) to effectively track visual targets in a confined environment while ensuring stability. The method allows for intraluminal navigation of TSDCRs along narrow lumens. To account for the presence of external outliers, a robust Jacobian estimation method is proposed, wherein improved iterative reweighted least squares with sliding windows are used to online calculate the robot's Jacobian matrix from sensing data. The estimated Jacobian establishes the motion relationship between the visual feature and the actuation. Furthermore, an optimal visual control method based on quadratic programming (QP) is designed for visual target tracking, while considering the robot's physical constraint and control constraints. The MLOVC method for visual tracking provides a reliable alternative that does not rely on the precise kinematics of TSDCRs and takes into consideration the impact of outliers. The control stability of the proposed approach is demonstrated through Lyapunov analysis. Simulations and experiments are conducted to evaluate the effectiveness of the MLOVC method, and the results demonstrate that it enhances tracking performance in terms of accuracy and stability.
Keyword ：
Continuum robot Continuum robot Optimal visual control Optimal visual control Robust Jacobian estimate Robust Jacobian estimate Stability analysis Stability analysis
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| GB/T 7714 | Pan, Chuanchuan , Deng, Zhen , Zeng, Chao et al. Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments [J]. | MECHATRONICS , 2024 , 104 . |
| MLA | Pan, Chuanchuan et al. "Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments" . | MECHATRONICS 104 (2024) . |
| APA | Pan, Chuanchuan , Deng, Zhen , Zeng, Chao , He, Bingwei , Zhang, Jianwei . Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments . | MECHATRONICS , 2024 , 104 . |
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