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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Zhang, Shengjie Sun, Xudong Song, Zhongru Guo, Yifei Qu, Juntian Li, Zhenkun School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China North China University of Science and Technology Department of Mechanical Engineering Tangshan063210 China Hebei Industrial Robot Industry Technology Research Institute Tangshan063000 China Shenzhen International Graduate School Tsinghua University Shenzhen518055 China

ISBN: (纸本)9798350320695

Combining the 'programmable' characteristics of smart materials with 3D printing technology can realize the integration of structural design and manufacturing. Magnetic controlled smart fluid owns the characteristics of flexible solid-liquid transformation, high driving efficiency and high safety. Soft robot driven by magnetic controlled intelligent fluid can realize a bionic movement similar to that of single-celled organisms. In this paper, we propose a reversible 3D printing method of magnetic phase transformation using a similar mechanism of sol-gel transformation in protoplasm. A magnetically controlled Pickering emulsion system with strong thixotropic properties was constructed and its microscopically self-assembled structure was characterized. The yielding and memory recovery characteristics of the magnetic-controlled memory fluid (MMF) were studied by means of steady-state and dynamic shear measurement, and the optimal rheological parameters suitable for printing were determined. A 3D printing platform and a memory elimination platform for coordinated control of magnetic field and motion system were built to further study and optimize the printing process. Finally, the composite structure is controlled by an array magnetic field to realize an amoeba-like motion. Novel voxel-based 3D printing and its application in soft robots can provide new ideas for space exploration, biomedicine, military reconnaissance and many other fields. © 2023 IEEE.

关键词： Emulsification

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A Dynamic Operational Optimization Method for robotic Mobile Fulfillment Systems with Inventory Discrepancy Events

A Dynamic Operational Optimization Method for Robotic Mobile...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ma, Huai Zhao, Ziyan Liang, Jiaqi Li, Xingyang Liu, Shixin Northeastern University Department of Information Science and Engineering Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan528311 China Polytechnique Montréal Département de Mathématiques et Génie Industriel Montréal Canada Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China School of Control Engineering Northeastern University at Qinhuangdao Hebei Qinhuang-dao066004 China

ISBN: (纸本)9781665410205

A robotic Mobile Fulfillment System (RMFS) is an emerging 'cargos-to-person' picking system that relies on the broom of Internet of Things (IoT) technology. It aims to offer significant enhancements in order picking efficiency. However, dynamic disturbances, such as inventory discrepancies arising from errors in receiving, shipping, and handling of goods, often disrupt its operations, leading to degraded service and increased operational costs, thereby affecting overall system performance. Traditional optimization solutions may necessitate adaptations or overhauls in response to such disturbances. This paper introduces a proactive multi-pathway response algorithm tailored to mitigating dynamic disturbances in RMFS, particularly concerning inventory discrepancies. We extend an open-source simulation framework to evaluate the performance of the proposed algorithm and conduct a comparative analysis of dynamic systems. Experimental results indicate that our proposed algorithm can effectively improve the processing efficiency of abnormal orders with the minimal system-wide impact, highlighting its potential to well address dynamic and abnormal events in smart warehouses. © 2024 IEEE.

关键词： Intelligent robots

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Design and Kinematic Characterisation of an Eight-Legged Bionic robot

SSRN

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SSRN 2025年

作者： Zhao, Jianwei Gao, Jiaping Zhai, Hao Zhang, Jikai Pei, Xu 's Mining Robot Research Center China China University of Mining and Technology Beijing China Measurement and Control Technology and Instruments from China University of Mining and Technology Beijing China The Department of Mechanical Engineering The Department of Mechanical Engineering Tsinghua University China China

The urgent need for complex terrain adaptability in industrial automation and disaster relief has highlighted the great potential of octopedal wheel-legged robots. However, their design complexity and motion control challenges must be addressed. In this study, an innovative design approach is employed to construct a highly adaptive robot architecture capable of intelligently adjusting the wheel-leg configuration to cope with changing environments. Advanced kinematic analysis and simulation techniques are combined with inverse kinematic algorithms and dynamic planning to achieve a typical ‘Step-wise Octopedal Dynamic Coordination Gait’ and different gait planning and optimisation. The effectiveness of the design and control strategy is verified through the construction of an experimental platform and field tests, significantly improving the robot's adaptability and mobility in complex terrain. This achievement not only enriches the theory and methodology of multi-legged robot design but also establishes a solid foundation for its widespread application in disaster rescue, exploration, and industrial automation. The octopedal wheel-legged robot is expected to become a leader in the field of robotics and play an increasingly critical role in the future. © 2025, The Authors. All rights reserved.

关键词： Disaster prevention

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Parametric Optimization of a Permanent Magnet Driver for Implantable Intramedullary Nail 23rd

Parametric Optimization of a Permanent Magnet Driver for Imp...

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23rd IFToMM China International Conference on Mechanism and Machine Science and engineering, IFToMM CCMMS 2022

作者： Lee, ShiKeat Nie, Zhenguo Xu, Handing Hu, Kai Lin, Hanwei Liu, Xin-Jun The State Key Laboratory of Tribology Department of Mechanical Engineering Tsinghua University Beijing100084 China Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control Tsinghua University Beijing100084 China Jiangsu Key Lab of Special Robot Technology Hohai University Jiangshu Changzhou213022 China

ISBN: (纸本)9789811993978

An intramedullary lengthening nail avoids complications frequently found in the external fixator and shows remarkable clinical results. The magnetic drive is the mainstream actuation method for intramedullary lengthening nails which shows good results, but clinicians claim insufficient distraction torque of the intramedullary lengthening nail will occur occasionally. In this paper, we investigate the optimal parameters for magnetic drive intramedullary lengthening nail based on parametric modeling to ensure the rotor magnet is rotated synchronously and the distraction torque is sufficient for bone lengthening. The driver-rotor magnet model is established, and parametric optimization of the angular position of the driver magnet local coordinate system, the angular velocity of the driver magnet, the number of driver magnets, and the number of pole pairs of the driver magnet to determine optimal values. Optimal parameters are selected and verified by experiments. The results show that a pair of single-pole pairs of the driver magnet rotating with an angular speed of 50 rpm can meet the minimal distraction torque of 1.5 N mm, which is around 5.6 N mm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Permanent magnets

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Nematode-Inspired Cable Routing Method for Cable Driven Redundant Manipulator*

Nematode-Inspired Cable Routing Method for Cable Driven Redu...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Hoyoung Kim Hosu Lee Jungwon Yoon Gwangju Institute of Science and Technology(GIST) Gwangju Republic of Korea Department of Control and Robot Engineering Gyeongsang National University Gyeongsangnam-do Republic of Korea

Cable driven redundant manipulator (CDRM) can provide complex movements with high dexterity and singularity reduction. However, traditional CDRMs with universal joints have the disadvantages of requiring a high number of motors and having a narrow joint workspace. Furthermore, there is a limitation in terms of stiffness and payload. Recently, CDRMs composed of Quaternion joints have been developed to address these disadvantages. They require fewer motors and have larger joint workspace due to the Quaternion joints. Yet, their cable routing method is the same as the traditional CDRMs. In this paper, we propose a novel nematode-inspired cable routing method to achieve complex movements and stiffness increase. To achieve the stiffness increase of CDRM, the proposed cable routing method was inspired by the alternately arranged muscle structure of nematodes. Moreover, moving pulley structure was selected to amplify the stiffness and force of CDRM. An 8-DOF CDRM prototype composed of four Quaternion joints was developed to show the effectiveness of the cable routing method. Kinematics simulation was conducted and then, verified by trajectory through experiments. Finally, a joint stiffness simulation was conducted and verified with the developed prototype by stiffness experiments.

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Deep Reinforcement Learning-Based Decision Making of Lane Change Considering Rear Vehicle Deceleration

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Journal of Institute of control, robotics and Systems 2022年第6期28卷 602-607页

作者： Jo, Gi-Hwan Park, Tae-Hyoung Department of Control Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent Systems and Robotics Chungbuk National University Korea Republic of

This paper proposes a lane change judgment method based on deep reinforcement learning considering rear vehicle deceleration. In existing autonomous driving, the lane change problem focuses on maintaining speed or avoiding collisions. However, an accident may occur if the deceleration of the vehicle behind is not considered when changing lanes. In this paper, the network was designed to solve the lane change problem by applying the DQN (Deep Q-Network) and the Deep Sets network. Moreover, the compensation was designed based on the maintained speed and deceleration of the rear vehicle. A four-lane vehicle-only road environment was implemented as a simulation and compared with the existing method. The proposed method maintained speed and lowered the deceleration rate of the rear vehicle compared with the existing method. © ICROS 2022.

关键词： Reinforcement learning

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Dissipative filtering for semi-Markov jump singularly perturbed systems with a dynamic event-triggered mechanism

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Journal of the Franklin Institute 2025年第9期362卷

作者： Liang, Kun Yan, Haoxuan Zhang, Chuanlin Mao, Jianliang Chen, Mengshen School of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Power Electronics and Motion Control of Anhui Higher Education Institutions Maanshan243032 China Anhui Province Key Laboratory of Special Heavy Load Robot Maanshan243032 China

The dissipative filter is designed in this paper via using a dynamic event-triggered approach for semi-Markov jump singularly perturbed systems. Based on the sampled-data, a novel dynamic event-triggered mechanism (ETM) is presented, where the threshold parameter dynamically adjusts according to continuously varying system output errors and modes. This approach adapts the threshold magnitude at the beginning to achieve better transient-time performance. The dynamic ETM offers more flexibility in data transmission, balancing system performance with transmission frequency. Furthermore, dissipative filter gains are designed by introducing non-singular matrices, eliminating the need for singular perturbation parameters. This method avoids ill-conditioning in the system and the singular perturbation parameter's upper bound is estimated. Sufficient conditions for stochastically stable and desired dissipative performance of the filter error system are shown using the Lyapunov theory. Finally, the effectiveness of the proposed approach is validated through a numerical example and an RC circuit system. © 2025 The Franklin Institute

关键词： Markov processes

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Diffusion-Driven Self-Supervised Learning for Shape Reconstruction and Pose Estimation

arXiv

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arXiv 2024年

作者： Sun, Jingtao Wang, Yaonan Feng, Mingtao Ding, Chao Shou, Mike Zheng Mian, Ajmal Saeed The College of Electrical and Information Engineering The National Engineering Research Centre for Robot Visual Perception and Control Hunan University Changsha410082 China Department of Electrical and Computer Engineering and Show Lab National University of Singapore Singapore117583 Singapore 2006 Australia

Fully-supervised category-level pose estimation aims to determine the 6-DoF poses of unseen instances from known categories, requiring expensive mannual labeling costs. Recently, various self-supervised category-level pose estimation methods have been proposed to reduce the requirement of the annotated datasets. However, most methods rely on synthetic data or 3D CAD model for self-supervised training, and they are typically limited to addressing single-object pose problems without considering multi-objective tasks or shape reconstruction. To overcome these challenges and limitations, we introduce a diffusion-driven self-supervised network for multi-object shape reconstruction and categorical pose estimation, only leveraging the shape priors. Specifically, to capture the SE(3)-equivariant pose features and 3D scale-invariant shape information, we present a Prior-Aware Pyramid 3D Point Transformer in our network. This module adopts a point convolutional layer with radial-kernels for pose-aware learning and a 3D scale-invariant graph convolution layer for object-level shape representation, respectively. Furthermore, we introduce a pretrain-to-refine self-supervised training paradigm to train our network. It enables proposed network to capture the associations between shape priors and observations, addressing the challenge of intra-class shape variations by utilising the diffusion mechanism. Extensive experiments conducted on four public datasets and a self-built dataset demonstrate that our method significantly outperforms state-of-the-art self-supervised category-level baselines and even surpasses some fully-supervised instance-level and category-level methods. The project page is released at Self-SRPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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FATUnetr:fully attention Transformer for 3D medical image segmentation

FATUnetr:fully attention Transformer for 3D medical image se...

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IEEE International Conference on Mechatronics and Automation

作者： QingFeng Li Jigang Tong Sen Yang Shengzhi Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of Technology Tianjin China Department of Electrical Engineering Tshwane University of Technology South Africa

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Recent studies have explored the applicability of the Transformer model in 3D medical segmentation tasks. The core attention mechanism of transformer, has the property of capturing remote dependencies, which is highly robust when segmenting medical images. Inspired by the emerging visual groupings in Vision Transformers (ViTs), self-attention can be made more robust by improving the intermediate representation. We propose a novel framework called FATUnetr that aims to take advantage of the full attention module to improve accuracy. We also reduce the number of parameters by applying deeply separable convolution at the bottleneck layer. We have verified the effectiveness in the extracranial vault multiple atlas labelling (BTCV) dataset for multi-organ segmentation.

关键词： Image segmentation Visualization Three-dimensional displays Attention mechanisms Accuracy Convolution Transformer cores

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