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IEEE international Conference on Mechatronics and automation

作者： Linshuai Zhang Yuyang Luo Xin Tian Shuoxin Gu Tao Jiang School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China School of Automation Chengdu University of Information Technology Chengdu China International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

This paper presents a three-dimensional force sensor based on fiber Bragg grating (FBG) with small size and high accuracy, which is proposed to be used in cardiac ablation procedures to achieve high-precision force feedback when the catheter tip contacts the tissue. The sensor consists of two hybrid elastomers for monitoring transverse and axial forces, as well as appropriately enhancing the deformation generated in the three directions to improve the sensitivity of the sensor. The structural body is configured with five suspended optical fibers, which are fixed with glue at both ends, and a preload is applied to ensure that the optical fibers are in a suspended state to avoid the chirp phenomenon. The article derives the theoretical deformation generated when the structural body is stressed, and proposes a decoupling method between the stressed temperature and deformation of the FBG fiber. In order to support further correction and improvement of the structural body performance and to further validate the feasibility of the structure, simulations based on finite element modeling were performed. The structure is configured to separate forces in three directions so that their measurements do not interfere with each other, thus maximizing decoupling between force components for highly accurate and high-resolution results.

关键词： Temperature measurement Optical fibers Optical fiber sensors Accuracy Sensitivity Deformation Force

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research on the seamless positioning method of an unmanned system based on UWB/IMU/GNSS fusion for indoor and outdoor

Research on the seamless positioning method of an unmanned s...

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international Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Tao Jiang Qian Wang Linshuai Zhang Shuoxin Gu Lin Xu Xianggui Tang School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu Sichuan School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu

This paper studies the multi-sensor fusion positioning methods of Ultra-wideband (UWB), Global Navigation Satellite System (GNSS) and Inertial measurement unit (IMU), and constructs a seamless positioning system based on UWB/IMU/GNSS multi-sensor fusion. In this paper, the indoor location information provided by UWB is fused with IMU location data through extended Kalman filter, which solves the problems of signal loss, track interruption and jump in positioning information when unmanned systems enter indoor environment, and also improves the positioning accuracy of the overall system. In the experiment of the indoor and outdoor scenes of the unmanned vehicle system, the trajectory diagram of each sensor combination positioning mode is counted, and the error of positioning information is analyzed and studied. The results show that the overall positioning error of this method is small, it can provide stable and reliable whole-time positioning information, and achieve the high-precision seamless positioning required by unmanned systems.

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Featherweight and high-precision closed-loop detection method

Featherweight and high-precision closed-loop detection metho...

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international Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Tao Jiang Xianggui Tang Ping Li Linshuai Zhang Shuoxin Gu Lin Xu Qian Wang Enzhi Zhu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Sichuan Province Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu

One of the key technologies for autonomous driving is localization. Precise localization is inseparable from high-precision maps. Simultaneous Localization And Mapping (SLAM) is technologies for building maps and locating in unknown environments. One of the mainstream directions of current autonomous driving research is how to carry out efficient closed-loop processing over long periods of time, eliminating accumulated error in the odometer. Improving the accuracy of the map and ensuring the accuracy of localization is crucial for ensuring the safety of autonomous driving. Therefore, this paper proposes a featherweight and high-precision method for multi-sensor fusion of laser and GPS for loop detection. The closed-loop results are optimized together with IMU to improve the mapping accuracy.

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A Novel Vascular Interventional Robot for Cardiac Ablation Surgery

A Novel Vascular Interventional Robot for Cardiac Ablation S...

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IEEE international Conference on Mechatronics and automation

作者： Jinshan Zuo Linshuai Zhang Xinlin Hao Yang Yang Xinyu Zhang Tao Jiang School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu Sichuan China School of Automation Chengdu University of Information Technology Chengdu Sichuan China International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu University of Information Technology Chengdu Sichuan China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

In recent years, force sensing and drive control at the distal end of ablation catheters have attracted widespread interest from scholars around the world. Among them, the challenge of high-precision operation of ablation catheters has attracted much attention. To address this problem, this study proposes a non-fixed modular ablation catheter clamping and pushing mechanism. The mechanism is based on a non-fixed modular design, aiming to avoid common under-clamping sliding errors in catheter operations, and achieves effective separation of the robot body and surgical instruments, allowing the ablation catheter to be quickly replaced and asynchronously sterilized. In addition, we conducted a modal analysis of the clamp-push structure, and the results showed that the modal frequency of the clamp-push structure is very low, so the catheter is not susceptible to the resonance effect of external excitation when it is pushed. This innovative design aims to further improve the accuracy, safety and intelligence of the vascular interventional surgical robot, providing reliable support for the success rate and safety of ablation surgery.

关键词： Medical robotics Modal analysis Surgery Resonant frequency Robot sensing systems Reliability engineering Resonance

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Point Cloud based Motion State Estimation Method for Autonomous Driving

Point Cloud based Motion State Estimation Method for Autonom...

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international Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Tao Jiang Enzhi Zhu Sidong Wu Linshuai Zhang Shuoxin Gu Lin Xu Qian Wang Xianggui Tang School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu

Generally, motion vector information primarily stems from moving objects, while static objects contribute minimally to the estimation task. Traditional approaches for motion vector estimation typically rely on scene flow methods that depend on deep models to extract features from individual points at a high cost. These methods then acquire flow information through complex matching mechanisms or feature decoding. Such approaches are computationally expensive and exhibit substantial latency. Moreover, they neglect the importance of motion objects in motion vector estimation and the interference from static objects. Therefore, this paper introduces a novel method that first performs point cloud motion segmentation and subsequently estimates motion vectors. This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks.

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A Novel Acupuncture Needle Based on FBG for Quantitative Evaluation of Expert Manipulations

A Novel Acupuncture Needle Based on FBG for Quantitative Eva...

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IEEE international Conference on Mechatronics and automation

作者： Zhihang Yuan Linshuai Zhang Tao Jiang Shuoxin Gu Lin Xu Yujie Zhang Peng Zeng School of Automation Chengdu University of Information Technology Chengdu Sichuan China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu Sichuan China International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu University of Information Technology Chengdu Sichuan China

In the process of acupuncture treatment, it is very important to obtain the pressure information and torque of acupuncture, which can be fed back to the acupuncturist to evaluate the therapeutic effect. At present, most of the sensor needles use resistance strain gauge and piezoelectric ceramic plate to complete force information collection. The above sensors are not only susceptible to the influence of external environment, but their accuracy is not high enough. Therefore, Fiber Bragg grating (FBG) is creatively used as the sensor in the field of acupuncture. It also has innovative significance in the field of acupuncture. In this paper, a three-axis force-sensing needle based on flexural hinge and Fiber Bragg grating (FBG) is designed to sense the contact force and torque when applying twist stitch method. To ensure the feasibility of the sensor needle, a mathematical model was established and an appropriate stiffness structure was designed. The rationality of the sensor needle was verified by static structure analysis and modal analysis of finite element simulation. Finally, it was proved that the sensor needle could meet the design requirements and complete the task of force sensing.

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A Force Control Method of Medical Robot Based on Impedance Control

A Force Control Method of Medical Robot Based on Impedance C...

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IEEE international Conference on robotics and Biomimetics

作者： Zhihang Yuan Linshuai Zhang Tao Jiang Shuoxin Gu Lin Xu Yujie Zhang Peng Zeng Marcin Grzegorzek School of Automation Chengdu University of Information Technology Chengdu Sichuan China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu Sichuan China International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu University of Information Technology Chengdu Sichuan China Institute of Medical Informatics University of Lübeck Lübeck Germany

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The problem of dynamic contact between robots and the physical environment has always been a hot research topic with great significance. In the process of rapid development of robotics technology, the field of medical robots has gradually emerged, and the research on compliant control and force control algorithms involved in dynamic contact has become increasingly important. At present, most of the subjects interacting with the environment and moving according to feedback are robotic arms. This paper focuses on a lighter and simpler type of actuator composed of a motor and slide table, studies the commonness of this kind of mechanism, and gives the power control method, and verifies its feasibility through Simulink simulation.

关键词： Analytical models Medical robotics Software packages Heuristic algorithms Power control Transfer functions Motors Mathematical models Steady-state Force control

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Multi-model Fusion on Real-time Drowsiness Detection for Telemetric robotics Tracking Applications

Multi-model Fusion on Real-time Drowsiness Detection for Tel...

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international Conference on Advanced robotics and intelligent Systems (ARIS)

作者： Ren C. Luo Chin Hao Hsu Yu-Cheng Wen International Center of Excellence in Intelligent Robotics and Automation Research (NTU-iCeiRA) National Taiwan University Taipei Taiwan

ISBN: (数字)9781728198231

ISBN: (纸本)9781728198248

Drowsiness of driver is one of the common causes resulting in road crashes. According to the research, there have been twenty percent of the road accidents which are related to the drowsiness of drivers. Nowadays, with the development technology, various approaches are introduced to detect the drowsiness of drivers. In this paper, we propose a multi-model fusion system which is composed of the three models to capture driver's face and detect drowsiness in the real-time for telemetric robotics tracking applications. The sensor device we used is an RGB camera which is mounted in front of driver to obtain the facial image. Then, we combine the results based on the state of the eye blink, yawn and head deviation to determine whether the driver is drowsy. We test our models to obtain the weighting factors in drowsy value. In the experiment, we show that our system has the high accuracy of detection.

关键词： Vehicles Mouth Faces Computational modeling Electroencephalography Feature extraction

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Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation

arXiv

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arXiv 2021年

作者： Zhang, Tiantian Wang, Xueqian Liang, Bin Yuan, Bo The Intelligent Computing Lab Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Research Center for Navigation and Control Department of Automation Tsinghua University Beijing100084 China

The powerful learning ability of deep neural networks enables reinforcement learning agents to learn competent control policies directly from continuous environments. In theory, to achieve stable performance, neural networks assume i.i.d. inputs, which unfortunately does no hold in the general reinforcement learning paradigm where the training data is temporally correlated and non-stationary. This issue may lead to the phenomenon of "catastrophic interference" and the collapse in performance. In this paper, we present IQ, i.e., interference-aware deep Q-learning, to mitigate catastrophic interference in single-task deep reinforcement learning. Specifically, we resort to online clustering to achieve on-the-fly context division, together with a multi-head network and a knowledge distillation regularization term for preserving the policy of learned contexts. Built upon deep Q networks, IQ consistently boosts the stability and performance when compared to existing methods, verified with extensive experiments on classic control and Atari tasks. The code is publicly available at: https://***/Sweety-dm/Interference-aware-Deep-Q-learning. © 2021, CC BY-NC-SA.

关键词： Reinforcement learning

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Catastrophic interference in reinforcement learning: A solution based on context division and knowledge distillation

TechRxiv

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TechRxiv 2021年

作者： Zhang, Tiantian Wang, Xueqian Liang, Bin Yuan, Bo The Intelligent Computing Lab Division of Informatics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Research Center for Navigation and Control Department of Automation Tsinghua University Beijing100084 China

The powerful learning ability of deep neural networks enables reinforcement learning (RL) agents to learn competent control policies directly from high-dimensional and continuous environments. In theory, to achieve stable performance, neural networks assume i.i.d. inputs, which unfortunately does no hold in the general RL paradigm where the training data is temporally correlated and non-stationary. This issue may lead to the phenomenon of "catastrophic interference" (a.k.a. "catastrophic forgetting") and the collapse in performance as later training is likely to overwrite and interfer with previously learned good policies. In this paper, we introduce the concept of "context" into the single-task RL and develop a novel scheme, termed as Context Division and Knowledge Distillation (CDaKD) driven RL, to divide all states experienced during training into a series of contexts. Its motivation is to mitigate the challenge of aforementioned catastrophic interference in deep RL, thereby improving the stability and plasticity of RL models. At the heart of CDaKD is a value function, parameterized by a neural network feature extractor shared across all contexts, and a set of output heads, each specializing on an individual context. In CDaKD, we exploit online clustering to achieve context division, and interference is further alleviated by a knowledge distillation regularization term on the output layers for learned contexts. In addition, to effectively obtain the context division in high-dimensional state spaces (e.g., image inputs), we perform clustering in the lower-dimensional representation space of a randomly initialized convolutional encoder, which is fixed throughout training. Our results show that, with various replay memory capacities, CDaKD can consistently improve the performance of existing RL algorithms on classic OpenAI Gym tasks and the more complex high-dimensional Atari tasks, incurring only moderate computational overhead. © 2021, CC BY.

关键词： Distillation

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