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Chinese Automation Congress (CAC)

作者： Yiqing Wang Changqing Xia Xi Jin Chi Xu Yanzhu Zhang School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive Control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive Control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities.

关键词： Fluctuations Trajectory tracking Simulation Noise Neural networks Manipulators Real-time systems Trajectory Manufacturing Predictive control

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A Novel ADRC Control Strategy for PWM Rectifier

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IFAC-PapersOnLine 2023年第2期56卷 422-427页

作者： Wei Wang Zixin Huang Ziang Wei Lejun Wang School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing China

Aiming at current harmonics, disturbance immunity and energy efficiency of pulse width modulation (PWM) rectifier system, a novel active disturbance rejection control (ADRC) strategy based on equivalent input disturbance (EID) is proposed. The circuit equations of the system are established, and then the underactuated characteristics of the model are used to design a dual closed-loop control strategy. Based on the conventional ADRC voltage loop controller, a new nonlinear error state feedback function is constructed by using the error estimation of the EID control to design a improved ADRC control strategy. The current loop adopts sliding mode controller. Simulation models are built by Matlab/Simulink, and conducted comparative tests. The results demonstrate that the novel ADRC control strategy has better performance than the traditional ADRC control strategy, faster DC voltage response rate, higher power factor and better results in resisting voltage disturbance and load disturbance.

关键词： PWM rectifier Underactuated characteristics Equivalent input interference Active disturbance rejection control Sliding mode control

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Markerless Visual Servoing Using Virtual Face Tag and Image Moment Invariants 4

Markerless Visual Servoing Using Virtual Face Tag and Image ...

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Zubair, Arif Fu, Yili Sohail, Amjad Khadija, Anwar Harbin Institute of Technology State Key Laboratory of Robotics and Systems Mechanical Engineering Department Harbin City China Space and Upper Atmosphere Research Commission Divisional Head TandD Hr Directorate General Karachi Pakistan Ned University of Engineering and Technology Industrial and Manufacturing Department Karachi Pakistan

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

A novel normalized-centered image moment invariant-based visual servoing scheme is presented. A fresh combination of the moments invariants is proposed for planar symmetrical objects that are parallel to the image plane at the desired position. The output of the face detection module in the image plane, that is a rectangle around the face is used as a virtual target, treated as a planar symmetrical object which always remains parallel to the image plane. The proposed method completely decouples the DoF and is 2D translation, rotation, and scaling invariant. It was experimentally validated on a real robotic arm using a haar cascade-based face detection system to accomplish visual servoing tasks for both 2D printed faces and actual 3D faces. Unlike traditional deep learning based visual servoing schemes, the proposed markerless visual servoing technique does not require a 3D model of the face or a reference image/pose at the desired position and it can also work with unfamiliar faces without prior training. © 2022 IEEE.

关键词： Face recognition

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Adaptive observer for a nonlinear system with partially unknown state matrix and delayed measurements

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IFAC-PapersOnLine 2023年第2期56卷 8702-8707页

作者： Olga Kozachek Alexey Bobtsov Nikolay Nikolaev Department of Control Systems and Robotics ITMO University Kronverkskiy av. 49 Saint-Petersburg 197101 Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia

Problem of an adaptive state observer design for nonlinear system with unknown time-varying parameters and under condition of delayed measurements is considered. State observation problem was raised by many researchers (see for example Sanx et al. (2019)). In this paper the results proposed in Bobtsov et al. (2021b), Bobtsov et al. (2021a), Bobtsov et al. (2022a), Bobtsov et al. (2022b) are developed. The problem is solved under assumption that the state matrix can be represented as sum of known and unknown parts. The output vector is measured with a known constant delay. An adaptive observer which reconstructs unmeasured state and unknown time-varying parameter is proposed.

关键词： nonlinear system delay adaptive observer parameter identification

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED

A High-Precision Edge Detection Algorithm Based on Improved ...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Yeborui Yuan Wenyuan Chen Jie Tang Jia Yang School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology.

关键词： Automation Image edge detection Image processing Neural networks Control systems Iterative algorithms Intelligent systems

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Self-Organized UAV Flocking Based on Proximal Control

Self-Organized UAV Flocking Based on Proximal Control

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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Amorim, Thulio Nascimento, Tiago Petracek, Pavel De Masi, Giulia Ferrante, Eliseo Saska, Martin Laboratory of Systems Engineering and Robotics Universidade Federal da Paraíba Czech Technical University in Prague Vrije Universiteit Amsterdam Czech Technical University in Prague

ISBN: (纸本)9780738131153

In this work, we address the problem of achieving cohesive and aligned flocking (collective motion) with a swarm of unmanned aerial vehicles (UAVs). We propose a method that requires only onboard sensing of the relative range and bearing of neighboring UAVs, and therefore requires only proximal control for achieving formation. Our method efficiently achieves flocking in the absence of any explicit orientation information exchange (alignment control), and achieves flocking in a random direction without externally provided directional information. To implement proximal control, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions. Our approach may be used independently from any external positioning system such as GNSS or Motion Capture, and can therefore be used in GNSS-denied environments. The performance of the approach was tested in real-world conditions by experiments with UAVs that rely only on a relative visual localization system called UVDAR, proposed by our group. To evaluate the degree of alignment and cohesiveness, we used the order metric and the steady-state value. © 2021 IEEE.

关键词： Global positioning system

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Dynamic Obstacle Avoidance for Magnetic Helical Microrobots Based on Deep Reinforcement Learning

Dynamic Obstacle Avoidance for Magnetic Helical Microrobots ...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yukang Qiu Yaozhen Hou Haotian Yang Yigao Gao Hen-Wei Huang Qing Shi Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Laboratory for Translational Engineering Harvard Medical School Cambridge MA USA Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Magnetic helical microrobots hold immense promise in biomedical domains owing to their compact size and efficient propulsion capabilities. However, navigating these microrobots through dynamic and unstructured environments, particularly when encountering numerous dynamic obstacles, remains a formidable challenge. In the study, a control framework based on deep reinforcement learning (DRL) with the objective of guiding a microrobot through dynamic obstacles towards specified target goals is introduced. Initially, we design and fabricate a microdrill capable of propulsion via external magnetic rotating fields produced by our magnetic actuation system. Subsequently, we construct a custom training environment, adhering to the OpenAI gym interface, to serve as the simulator for training purposes. Utilizing the proximal policy optimization algorithm, we conduct training of the navigation policy within this simulator. Simulations and experimental validations conducted in dynamic environments affirms the efficacy of the proposed method.

关键词： Training Navigation Magnetic cores Heuristic algorithms Magnetic domains Propulsion Deep reinforcement learning

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Xinqiang Guo Yupeng Zou Shuo Zhang Mingxiang Luo Worawarit Kobsiriphat Xinyu Wu Meng Yin Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering China University of Petroleum East China National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.

关键词： Support vector machines Deep learning Accuracy Noise Gesture recognition Stroke (medical condition) Feature extraction Electromyography Convolutional neural networks Long short term memory

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration

Design of Capsule Robot for Magnetic Gastric Biopsy and Drug...

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IEEE International Conference on Mechatronics and Automation

作者： Zhihui Xu Zongze Li Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion.

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Magnetically Driven Capsule Robot for Multi-Targeted Biopsy and Drug Delivery in Stomach

Magnetically Driven Capsule Robot for Multi-Targeted Biopsy ...

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IEEE International Conference on robotics and Biomimetics

作者： Zhihui Xu Tanyong Wei Zongze Li Ding Huang Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising solution for non-invasive and painless screening of gastrointestinal diseases, offering significant advantages in patient comfort and reduced risk of cross-infection. Despite these advantages, existing capsule robots fall short in their ability to conduct targeted biopsies and deliver drugs to specific gastric lesions. To address this limitation, this paper presents the design and development of a magnetically driven capsule robot that leverages visual feedback to achieve accurate targeting for biopsy and drug administration in gastric lesions. Internal magnets, responsive to an external gradient magnetic field, enable the controlled propulsion of the capsule robot. A kinetic model of the capsule robot is analyzed, allowing it to recognize lesion contours through the endoscopic camera. This recognition determines the rotation angle or small adjustments required for the capsule robot's attitude and positioning. Experimental results in a simplified in vitro stomach model showcase the capability of the capsule robot to achieve targeted biopsy and drug delivery. These results highlight the potential feasibility of the proposed capsule robot in enhancing diagnostic accuracy and treatment efficacy with broader applications within the realm of gastric healthcare.

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