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IEEE International Conference on Mechatronics and Automation

作者： Tianqi Xia Enzeng Dong Sen Yang Jigang Tong Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

In this paper, facing the aerial transportation task with a large volume cargo, the system of quadrotor with a cable-suspended rigid payload is investigated. The dynamic model of proposed system is built with Lagrange d’Alembert principle. The dynamic model is linearized to design the feedback control method. Finally, two groups of numerical simulation with proposed feedback controller are designed to demonstrate the effectiveness of system model and the results of simulation illustrate satisfactory performance.

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Classification of EEG signals based on CNN-Transformer model

Classification of EEG signals based on CNN-Transformer model

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IEEE International Conference on Mechatronics and Automation

作者： Jianwei Liu Enzeng Dong Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

Brain-computer interfaces (BCI) based on EEG have attracted extensive research and attention worldwide, while motor imagery (MI), mental arithmetic (MA), and P300 event-related potentials are a few of the more commonly used *** Transformer(ViT) is a new Transformer model that has superior global processing power compared to Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).In this study, we propose a hybrid CNN-Transformer based model that uses CNN to convolve EEG signals in time and space, followed by ViT for global processing, and finally optimizes the model using 10-run $\times 10$-fold cross-validation and validates it on a publicly available dataset of 29 subjects. Final accuracies of 87.23% and 90.79% were achieved on the MI and MA tasks, respectively. Compared to other literature, the model achieved higher classification accuracies.

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MAN: Mix-domain attentional single-stage object detection network with multiple attention mechanisms

MAN: Mix-domain attentional single-stage object detection ne...

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IEEE International Conference on Mechatronics and Automation

作者： Lei Sun Yun Han Xinxiang Gong Shuaiyu Jin School of Electrical Engineering and Automation Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China

Deep convolutional neural networks (CNN) have achieved remarkable progress in the image processing field during the past decade. However, most existing CNN-based object detection algorithms mainly delve into network-deepening, overlooking the exploration of the inherent relationship amongst the features. To address this issue, an end-to-end Mix-domain Attention Network (MAN) is proposed. First, the backbone and head of MAN adopt the structure of YOLOv5 (YOLOv5 is a family of object detection architectures and models pre-trained on the COCO dataset by Ultralytics). Then, we embed two key modules in the backbone and head: non-local blocks and stereoscopic attention group (SAG). Non-local blocks are different from CNN’s local operations in that they can capture long-distance spatial contextual information (Such as the pixel-to-pixel relationship in images, and the pixel-to-pixel relationship between different frames in video streams) and combined with the residual structure, this allows the feature dimensions of the input and output to be consistent. Subsequently, the features extracted from the non-local blocks will be further passed through a trainable stereoscopic attention group to calculate the correlation of channel-to-channel, pixel-to-pixel, and update the output features. The Non-local operation is the self-attention mechanism and the SAG operation is the channel and spatial attention mechanism. Our innovation is the combination of these two operations to form a Mix-domain attention mechanism. The Non-local increases the connection between the pixel points. The SAG allows the model to focus more on the places that need attention. The combination of these two modules enhances recognition accuracy. The experimental results demonstrate that the two versions of a Mixdomain Attention Network achieves the following results on Microsoft Common Objects in Context (MS-COCO) with an input size of 640*640: AP(Average Precision)=38.2%, 44.4%,AP05 (IOU >0.5 Average Precisi

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Design of control System for Lower Limb Rehabilitation Robot on the Healthy Side sEMG Signal 20

Design of Control System for Lower Limb Rehabilitation Robot...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yu, Shiqin Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

With the number of stroke patients increasing year by year, rehabilitation exoskeleton robot has been paid more and more attention. For the rehabilitation exoskeleton robot, human-computer interaction ability is an important index, which affects the effect of rehabilitation therapy to a great extent. Surface Electromyography (sEMG) signals are the combined effect of sEMG signals and electrical activities on nerve stem on the skin surface, which can reflect neuromuscular activities in advance and can be used to predict movement intention by sEMG signals. Therefore, this article proposes to use sEMG signals to monitor the motion information of the healthy leg in real-time, extract the characteristics of the electromyography signals, use the sEMG of the healthy leg as the control signal, reflect the motion patterns reflected by the sEMG collected from the healthy side, and then use the motion intention recognition method of Long Short Term Memory(LSTM) neural network to identify the motion intention of the prosthetic limb by identifying the motion patterns of the swing phase of the healthy side. The results indicate that the predicted maximum Root Mean Squared Error(RMSE) is 5.3729, which proves the feasibility of using LSTM model for motion intention recognition and contributes to the real-time and accuracy of lower limb exoskeleton rehabilitation. © 2023 IEEE.

关键词： Long short-term memory

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A DDPG-Based Method of Autonomous Catheter Navigation in Virtual Environment 20

A DDPG-Based Method of Autonomous Catheter Navigation in Vir...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Tian, Wei Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Vascular interventional surgery is the main method for treating cardiovascular diseases. But navigating endovascular catheters through the vascular tree is a highly challenging task even for highly trained specialists. Automation of this task can reduce the burden on surgeons and is expected to improve the surgical outcomes. Although there have been relevant studies utilizing reinforcement learning algorithms to realize autonomous navigation of catheter in the virtual environment Cathsim. However, the kinematics model of the catheter in Cathsim does not conform to the operating mode of the catheter in real vascular interventional surgery. Besides, there are problems such as low success rates of catheter autonomous navigation tasks. To address these issues, this paper modifies the kinematics model of the catheter in Cathsim and designs a catheter autonomous navigation model based on reinforcement learning DDPG (Deep Deterministic Policy Gradient) algorithm. The experimental results show that the agents trained through DDPG in this paper performs better than the agents trained through PPO (Proximal Policy optimization) in other studies in terms of navigation task success rate, completion time, and contact force between the catheter and vascular wall during the navigation process. © 2023 IEEE.

关键词： Reinforcement learning

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Rapid calibration method for 3d laser scanner

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Recent Patents on Engineering 2020年第2期14卷 234-241页

作者： Liu, Bin Qiao, Qian Han, Fangfang Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin City300384 China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin City300384 China

Background: The 3D laser scanner is a non-contact active-sensing system, which has a number of applications. Many patents have been filed on the technologies for calibrating 3D laser scanner. A precise calibration method is important for measuring the accuracy of the 3D laser scanner. The system model contains three categories of parameters to be calibrated which include the camera intrinsic parameters, distortion coefficients and the light plane parameters. Typically, the calibration process is completed in two steps. Based on Zhangs method, the calibration of the camera intrinsic parameters and distortion coefficients can be performed. Then, 3D feature points on the light plane should precisely be formed and extracted. Finally, the points are used to calculate the light plane parameters. Methods: In this paper, a rapid calibration method is presented. Without any high precision auxiliary device, only one coplanar reference target is used. By using a group of captured images of the coplanar reference target placed in the field of view arbitrarily, calibration can be performed in one step. Based on the constraint from the planes formed by the target in different directions and the camera imaging model, a large amount of 3D points on the light plane can easily be obtained. The light plane equation in the camera coordinates system can be gathered by executing plane fitting to the 3D points. Results: During the experimental process, the developed 3D laser scanner was calibrated by the proposed method. Then, the measuring accuracy of the system was verified with known distance in vertical direction of 1mm with sequential shifting motion generated by precision translation stage. The average value of the measured distances was found to be 1.010mm. The standard deviation was 0.008mm. Conclusion: Experimental results prove that the proposed calibration method is simple and reliable. © 2020, Bentham Science Publishers. All rights reserved.

关键词： Calibration

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Study on the Active Movement Capsule Robot for Biopsy 17

Study on the Active Movement Capsule Robot for Biopsy

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Guo, Shuxiang Huang, Fang Guo, Jian Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory Application in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extention 391 Tianjin China

ISBN: (纸本)9781728164151

In this paper, a novel biopsy robot is proposed. In order to solve the problem of the single function of the commercial capsule endoscope, this paper proposes a novel biopsy capsule robot driven by an external magnetic field. The module includes two radial magnetized magnetic rings and a biopsy mechanism. Without considering the addition energy consumption, using the coupling effect of the three-axis Helmholtz coil and the robot's built-in magnet, the crank slider mechanism converts the torque acting on the magnet by a uniform rotating magnetic field into the cutting force during biopsy. The magnetic switch used to control the switching between the motion function of the anchor biopsy robot and the anchor biopsy function. Finally, the functional modules in the pipeline of anchored biopsy robot is verified by experiments. The results show that the biopsy robot can realize the robot's anchoring in the intestine while performing the biopsy function. The realization of the biopsy capsule robot has important medical diagnostic significance. © 2020 IEEE.

关键词： Biopsy

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Point Density-aware Channel-wise Transformer for LiDAR 3D Object Detection

Point Density-aware Channel-wise Transformer for LiDAR 3D Ob...

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IEEE International Conference on Mechatronics and Automation

作者： Fanhang Yang Enzeng Dong Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology 391 Binshui West Road Xiqing District Tianjin China Department of Electrical Engineering Tshwane University of Technology Pretoria South Africa

We present a two-stage 3D object detection framework from point clouds, named Point Density-aware Channel-wise Transformer (PD-CT3D), which investigate the property of point density. This architecture uses 3D sparse CNN to effectively generate high-quality proposals at the first stage, then integrates the inherent property of point density and 3D CNN-based voxel features by density-aware proposal grid pooling. Specifically, each generated proposal from the first stage is divided into grids to aggregate corresponding voxel-wise features and raw point-based features for encoding the representative features from the whole scene. Subsequently, a channel-wise encoder-decoder transformer is adopted to extract the encoded density-aware features and decode them into a global representation for final refinement. Experiments on the widely used KITTI dataset show that the PD-CT3D achieves competitive performance among state-of-the-art methods.

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Study on control Strategy of the Vascular Interventional Surgical Robot Based on LADRC 20

Study on Control Strategy of the Vascular Interventional Sur...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zhang, Wenqiang Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications Complicated Systems and Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

As a medical device used in surgery, the vascular interventional surgical robot itself needs to have good control accuracy to ensure the safety of the surgery process. Given this security problem, this paper selects the Line Active Disturbance Rejection control (LADRC) as the control strategy of the vascular interventional robot, aiming at improving the master-slave tracking accuracy of the vascular interventional robot. This article compares the LADRC and PID(Proportional-Integral-Derivative) control strategies through simulation and experiments. The experimental results show that LADRC has good control performance for master-slave tracking and can be applied to the vascular interventional surgical robots. © 2023 IEEE.

关键词： Robotic surgery

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Study on Autonomous Surgical control System of The Vascular Interventional Surgical Robot Based on Catheter Tip Location Tracking 20

Study on Autonomous Surgical Control System of The Vascular ...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Feng, Zhimin Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

The control system of vascular interventional surgical robot is a complex nonlinear system. When the catheter is inserted into human blood vessels, due to the irregular and curved shape of human blood vessels, the catheter will undergo small deformation, which will affect the displacement of the catheter. In order to reduce the displacement tracking error, a BP neural network PID controller was designed to compensate the displacement error of the autonomous vascular interventional surgical robot. Firstly, the dynamic analysis is carried out according to the axial displacement motion of the vascular interventional surgical robot, and the catheter displacement tracking controller was designed. According to the displacement error, BP neural network PID control is used to output three parameters of PID. Finally, in the experiment and PID control simulation comparison, the results show that the BP neural network PID control has a small displacement tracking error and superior tracking performance, which can meet the requirements. © 2023 IEEE.

关键词： Three term control systems

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