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Obstacle avoidance in human-robot cooperative transportation with force constraint

Science China(Information Sciences) 2023年第1期66卷 334-335页

作者： Ying ZHANG Chenguang YANG Sheng XU Yongsheng OU College of Automation Science and Engineering South China University of Technology Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences

Dear editor,Robots are often conceived as human-like machines that can interact with humans naturally and safely. Human-robot interaction means that robots can communicate with people,understand their needs, and act accordingly [1]. This interaction is critical when two people are needed to finish a task cooperatively.

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General Kinetostatic Modeling and Deformation Analysis of a Two-Module Rod-Driven Continuum Robot with Friction Considered

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Chinese Journal of Mechanical engineering 2023年第3期36卷 110-122页

作者： Peiyi Wang Xinhua Yang Xiangyang Wang Sheng Guo School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing 100044China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518005China

Continuum robots actuated by flexible rods have large potential applications,such as detection and operation tasks in confined environments,since the push and pull actuation of flexible rods withstand tension and compressive force,and increase the structure's *** this paper,a generalized kinetostatics model for multi-module and multi-segment continuum robots considering the effect of friction based on the Cosserat rod theory is ***,the model is applied to a two-module rod-driven continuum robot with winding ropes to analyze its deformation and load *** different in-plane configurations under the external load term as S1,S2,C1,and C2 are *** a bending plane as an example,the tip deformation along thex-axis of these shapes is simulated and compared,which shows that the load capacity of C1 and C2 is generally larger than that of S1 and ***,the deformation experiments and simulations show that the maximum error ratio without external loads relative to the total length is no more than 3%,and it is no more than 4.7%under the external *** established kinetostatics model is proven sufficient to accurately analyze the rod-driven continuum robot with the consideration of internal friction.

关键词： Rod-driven continuum robot Kinetostatic model Cosserat rod theory Deformation and stiffness analysis

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Image Data Augmentation through Generative Adversarial Networks for Waste Sorting

Image Data Augmentation through Generative Adversarial Netwo...

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2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025

作者： Bacchin, Alberto Marangoni, Fabio Gottardi, Alberto Menegatti, Emanuele University of Padova Intelligent Autonomous System Lab Department of Information Engineering Padua35131 Italy IT+Robotics Srl Vicenza36100 Italy

ISBN: (纸本)9798331516857

The growing volumes of solid waste present a significant challenge for sustainable management. An efficient robotics system for sorting waste materials is essential to improving recycling and contamination removal, but it is often limited by the extreme variation of items to recognize in a cluttered and dirty environment. To bring robots in such scenario, the system must be able to recognize and manipulate different objects, adapting to a high degree of variability. A system based on deep learning can achieve high performance and fulfill these requirements. However, learning models require extensive labeled data, which limits their applicability in this context. Indeed, the complexity of the real-world environment presents a significant challenge to effective data collection, highlighting the importance of data augmentation techniques for creating a suitable dataset for training models for object recognition in this context. To address this challenge, our study investigates the use of Generative Adversarial Networks (GANs) for synthetic data generation in waste-sorting systems. GANs are employed to produce synthetic images of waste streams with corresponding labels that accurately reflect the complexity and diversity of real-world waste. A primary con-cern in synthetic data generation is ensuring alignment between generated images and their labels. To address this, we introduce a novel method for controlling the GAN generation process, which enforces semantic coherence and preserves the intended structure of the labeled data. The experiments demonstrate that semantic segmentation models trained on datasets augmented with these synthetic images perform better in the semantic segmentation of waste. © 2025 IEEE.

关键词： Semantic Segmentation

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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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2024 International Convention on Rehabilitation engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Cao, Wujing Guo, Xinqiang Zou, Yupeng Zhang, Shuo Luo, Mingxiang Kobsiriphat, Worawarit Wu, Xinyu Yin, Meng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518005 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems China School of Mechanical Engineering China University of Petroleum China National Metal and Materials Technology Center Thailand

ISBN: (纸本)9798350355123

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. © 2024 IEEE.

关键词： Convolutional neural networks

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Integrating Task and Motion Planning for Manufacturing Processes 10

Integrating Task and Motion Planning for Manufacturing Proce...

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10th Italian Workshop on Artificial Intelligence and robotics, AIRO 2023

作者： Gottardi, Alberto Castaman, Nicola Pagello, Enrico Menegatti, Emanuele IT+Robotics srl Vicenza36100 Italy Intelligent Autonomous System Lab Dept. of Information Engineering University of Padova Padua35131 Italy Professional Industrial Consultant Bassano del Grappa36061 Italy

The synergy between the robot's abilities and human expertise provides several advantages in the industrial processes. A proper integration of Task and Motion Planning (TAMP), considering the interaction of the user with the environment is essential to maximize the benefits of robot-assisted tasks and to ensure safety in collaborative H-R tasks. However, the state of the art of the TAMP research field should be improved to overcome the limitations of the traditional TAMP approaches to be used in industrial scenarios. We illustrate a list of challenges to be solved in order to improve to manage efficiently the dynamism and uncertainty given by introducing a human operator into a robotized process of draping fiber carbon plies, with reference to the granted European Project DrapeBot. Finally, the proposed TAMP architecture is presented to address the challenges described. © 2024 Copyright for this paper by its authors.

关键词： Motion planning

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Research on multi-motor speed synchronous control based on mean coupling control 9

Research on multi-motor speed synchronous control based on m...

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9th International Symposium on Sensors, Mechatronics, and Automation system, ISSMAS 2023

作者： Wu, Xiong Wei, Shouqi You, Jinwei Sun, Jianquan School of Mechanical and Engineering Guilin University of Electronic Technology Guilin541004 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781510672802

In order to improve the tracking and synchronization performance of multi-motor synchronous system after disturbance in steady state operation, a multi-motor control system was proposed. Firstly, the multi-motor speed synchronization control structure was designed based on the traditional mean coupling control (MCC) structure. Then the particle swarm optimization and the improved variable speed integral term were introduced into it to optimize the regular compensation factors and the parameters of PI controller. The improved system was called MCC_PSO system. In MATlab/Simulink, we chose three permanent magnet synchronous motors (PMSM) to build the multi-motor synchronous control system model and from the results we can know the improved synchronous control system is superior to the traditional control system in both tracking performance and synchronization performance. © 2024 SPIE.

关键词： mean coupling multi-motor synchronous control particle swarm optimization variable speed integration

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Multiple Sensor Fusion-Based Positioning Robot for Steam Generator Inspection 2

Multiple Sensor Fusion-Based Positioning Robot for Steam Gen...

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2nd International Symposium on Sensor Technology and Control, ISSTC 2023

作者： You, Jingwei Wei, Shouqi Wu, Xiong Sun, Jianquan Guilin University of Electronic Technology Department of Electrical Engineering Guilin541004 China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518000 China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen518000 China

ISBN: (纸本)9798350314465

Nuclear energy is presently the clean energy source most likely to massively displace fossil energy, and the secondary side of the steam generator is an important part of a nuclear power plant. Due to the small space of the secondary side of the steam generator and the confinement of the vessel, the precise positioning of the robot inside the steam generator cannot be achieved by a single sensor alone. This paper introduces a multi-sensor fusion steam generator secondary side inspection robot. Primary emphasis is on data fusion of multiple sensors, such as laser sensors, cameras, IMU, and wheel encoder. First of all, this paper integrates data from wheel encoder data and IMU data by using extended Kalman filtering (EKF), which decreases the drift error during the motion and enhances the robot positioning precision. Afterwards, we incorporate laser ranging sensors and camera picture data to enhance the overall spatial sensitivity of the robot through particle filtering algorithms. Experiments show that the robot works reliably and has high positioning accuracy. © 2023 IEEE.

关键词： Steam generators

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Image Data Augmentation through Generative Adversarial Networks for Waste Sorting

Image Data Augmentation through Generative Adversarial Netwo...

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IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

作者： Alberto Bacchin Fabio Marangoni Alberto Gottardi Emanuele Menegatti Department of Information Engineering Intelligent Autonomous System Lab University of Padova Padua Italy IT+Robotics srl Vicenza Italy

ISBN: (数字)9798331516857

ISBN: (纸本)9798331516864

关键词： Waste materials Semantic segmentation Semantics Diversity reception Generative adversarial networks Data augmentation Generators Data models Sorting Synthetic data

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Exoskeleton-Based Lower Limb Rehabilitation Robot Design and Simulation for Children with Cerebral Palsy

Exoskeleton-Based Lower Limb Rehabilitation Robot Design and...

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2024 International Convention on Rehabilitation engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Cao, Wujing Zhang, Shuo Zou, Yupeng Guo, Xinqiang Li, Changyu Wang, Jingxin Kobsiriphat, Worawarit Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518005 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems China School of Mechanical Engineering China University of Petroleum China Jining Zhongke Intelligent Technology Co. Ltd. China Zhengzhou Central Hospital Zhengzhou University Department of Rehabilitation Medicine Zhengzhou China National Metal and Materials Technology Center Thailand

ISBN: (纸本)9798350355123

Squat gait is the most common abnormal gait in children with cerebral palsy. Gait training with lower limb rehabilitation robots can improve rehabilitation efficiency and reduce manual labor for them. A novel exoskeleton-based lower limb rehabilitation robot for children with cerebral palsy is proposed, considering the squat gait characteristics of children with cerebral palsy. The structure of the lower limb rehabilitation robot, which combines hip and knee with 4 active degrees of freedom exoskeleton and mobile walker, is designed and optimized. Static analysis of the key mechanical structure of the exoskeleton is conducted using ANSYS Workbench, and local structures are optimized to ensure the safety factor of the mechanical structure is greater than 1, guaranteeing reasonable structural strength and stiffness design. Dynamic simulation analysis based on the ADAMS model validates the reliability and dynamic walking effect of the lower limb exoskeleton rehabilitation robot, obtaining the curve relationship between joint angles and motion cycles, thus verifying the effectiveness of the mechanical system motion. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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Computer Modeling in engineering & Sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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