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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Chen, Yifei Yang, Deyu Liu, Ziyu Yang, Boyu Wang, Hongqiang Southern University of Science and Technology Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Shenzhen518055 China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Southern Marine Science and Engineering Guangdong Laboratory Guangzhou510000 China

ISBN: (纸本)9798350325706

Pipeline inspection poses challenges that require robots to navigate complex environments. However, existing pipeline robots with the rigid bodies often face limitations in terms of efficiency, flexibility, and adaptability considering the significant size variations and complex terrains in pipelines. This paper introduces a novel design of wheel pipeline robots that addresses these limitations by incorporating the Origami Anisotropic Stiffness Structure (OASS). Inspired by the desert iguana's skin, the OASS offers a unique combination of rigidity and flexibility in different directions, making it an ideal skeletal framework for the robot with only 146 g weight. By integrating the OASS with PLA and resin materials, we enhance the robot's support and enable it to effectively navigate diverse pipeline configurations. Experimental results demonstrate the robot's capability to maneuver through pipelines of different diameters, U-shaped turns, obstacles, and even vertical sections. This research provides a promising solution for inspection tasks in complex pipeline systems. © 2023 IEEE.

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Designing Unpowered Shoulder Complex Exoskeleton via Contralateral Drive for Self-rehabilitation of Post-stroke Hemiparesis

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Journal of Bionic engineering 2023年第3期20卷 992-1007页

作者： Ning Li Tie Yang Yang Yang Wenyuan Chen Peng Yu Chuang Zhang Ning Xi Ying Zhao Wenxue Wang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China University of Chinese Academy of Sciences Beijing100049China Emerging Technologies Institute Department of Industrial and Manufacturing Systems EngineeringUniversity of Hong KongPokfulamHong Kong999077China Department of General Surgery Shengjing Hospital of China Medical UniversityShenyang110004China

Rehabilitation using exoskeleton robots can effectively remediate dysfunction and restore post-stroke survivors’ physical ability. However, low kinematic compatibility and poor self-participation of post-stroke patients in rehabilitation restrict the outcomes of exoskeleton-based therapy. The study presents an Unpowered Shoulder Complex Exoskeleton (USCE), consisting of Shoulder Girdle Mechanism (SGM), Ball-and-Socket Joint Mechanism (BSM), Gravity Compensating Mechanism (GCM) and Adjustable Alignment Design (AAD), to achieve self-rehabilitation of shoulder via energy transfer from the healthy upper limb to the affected counterpart of post-stroke hemiplegic patients. The SGM and AAD are designed to improve the kinematic compatibility by compensating for displacements of the glenohumeral joint with the adaptable size of USCE for different wearers. The BSM and GCM can transfer the body movement and energy from the healthy half of the body to the affected side without external energy input and enhance the self-participation with sick posture correction. The experimental results show that the USCE can provide high kinematic compatibility with 90.9% movement similarity between human and exoskeleton. Meanwhile, the motion ability of a post-stroke patient’s affected limb can be increased through energy transfer. It is expected that USCE can improve outcomes of home-based self-rehabilitation.

关键词： Unpowered exoskeleton Health-paralysis combination Energy transfer Self-rehabilitation Shoulder mechanism design

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Supernumerary Robotic Limbs to Augment Astronauts Performing Post-Fall Recoveries during Partial-Gravity Spacewalks

Supernumerary Robotic Limbs to Augment Astronauts Performing...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Ballesteros, Erik Carpenter, Kalind Asada, H. Harry Department of Mechanical Engineering Massachusetts Institute of Technology Course 2 CambridgeMA02139 United States Advanced Robotics Systems Group 347Z NASA Jet Propulsion Laboratory PasadenaCA91011 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper investigates the effectiveness of a type of wearable robot, known as Supernumerary Robotic Limbs (SuperLimbs) in assisting an astronaut performing partial-gravity ExtraVehicular Activities (EVAs). SuperLimbs can directly address a key technology gap identified by NASA concerning that of incapacitated crew rescue devices during partial-gravity EVAs. To contextualize SuperLimbs in human spaceflight application, we start by reviewing other forms of wearable robotics technologies and perform a qualitative comparison with SuperLimbs. From there, we present a suite of operational modes that SuperLimbs is capable of delivering in a suited partial-gravity EVA. Within the operational mode suite, we selected the most urgent use-case: that of performing a post-fall recovery, and reviewed the results from a pilot-human study. To validate the effectiveness of SuperLimbs in providing the necessary assistive forces needed for a post-fall recovery, a series of validation experiments was performed with a prototype SuperLimbs system. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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Data-Driven Dynamics Modeling of Miniature Robotic Blimps Using Neural ODEs With Parameter Auto-Tuning

arXiv

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arXiv 2024年

作者： Zhu, Yongjian Cheng, Hao Zhang, Feitian State Key Laboratory of Turbulence and Complex Systems the Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China

Miniature robotic blimps, as one type of lighter-than-air aerial vehicles, have attracted increasing attention in the science and engineering community for their enhanced safety, extended endurance, and quieter operation compared to quadrotors. Accurately modeling the dynamics of these robotic blimps poses a significant challenge due to the complex aerodynamics stemming from their large lifting bodies. Traditional first-principle models have difficulty obtaining accurate aerodynamic parameters and often overlook high-order nonlinearities, thus coming to their limit in modeling the motion dynamics of miniature robotic blimps. To tackle this challenge, this letter proposes the Auto-tuning Blimp-oriented Neural Ordinary Differential Equation method (ABNODE), a data-driven approach that integrates first-principle and neural network modeling. Spiraling motion experiments of robotic blimps are conducted, comparing the ABNODE with first-principle and other data-driven benchmark models, the results of which demonstrate the effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Airships

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Design of a Remote Controlled Puppet Robot Imitating a Manual Driven Puppet Using Deep Learning Pose Detection

Design of a Remote Controlled Puppet Robot Imitating a Manua...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Hamed Hamzeh Kiarash Shahroozi Ahmad Nabipour Parham Kazemi Mohammad Malek-zahedi Mehdi Hallajian Hadi Moradia Advanced Robotics and Intelligent Systems Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Traditional puppet manipulation systems often require human operators to be physically present in tight and cramped locations. This leads to challenges in the positioning and effective operation of puppets, particularly in the presence of children in educational settings. In this paper, we present the design, development, and integration of a cost-effective robotic system for remotely manipulating puppets in real-time. Additionally, the system integrates a YOLOv8 Pose Detection model, trained on a custom dataset with an accuracy of 91.4%, enabling real-time gesture recognition and allowing the robot to be controlled by imitating the motions of a manually operated puppet. The system demonstrates the potential for many applications, including interactive entertainment, teleoperation, and education.

关键词： Hands Mechatronics Education Entertainment industry Manuals Gesture recognition Real-time systems Maintenance Robots Intelligent control

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration 20

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

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

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

ISBN: (纸本)9798350320831

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. © 2023 IEEE.

关键词： Biopsy

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Parallel Bounded Search for the Maximum Clique Problem

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Journal of Computer Science & Technology 2023年第5期38卷 1187-1202页

作者：江华白珂刘海姣李初民 Felip Manya 付樟华 Engineering Research Center of Cyberspace Yunnan UniversityKunming 650500China School of Software Yunnan UniversityKunming 650500China Laboratory of Modeling Information and SystemsUniversity of Picardie Jules VerneAmiens 80039France Artificial Intelligence Research Institute Spanish National Research CouncilCatalonia 08193Spain Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000China Institute of Robotics and Intelligent Manufacturing Chinese University of Hong KongShenzhen 518000China

Given an undirected graph,the Maximum Clique Problem(MCP)is to find a largest complete subgraph of the *** is NP-hard and has found many practical *** this paper,we propose a parallel Branch-and-Bound(BnB)algorithm to tackle this NP-hard problem,which carries out multiple bounded searches in *** search has its upper bound and shares a lower bound with the rest of the *** potential benefit of the proposed approach is that an active search terminates as soon as the best lower bound found so far reaches or exceeds its upper *** describe the implementation of our highly scalable and efficient parallel MCP algorithm,called PBS,which is based on a state-of-the-art sequential MCP *** proposed algorithm PBS is evaluated on hard DIMACS and BHOSLIB *** results show that PBS achieves a near-linear speedup on most DIMACS instances and a superlinear speedup on most BHOSLIB ***,we give a detailed analysis that explains the good speedups achieved for the tested instances.

关键词： Branch-and-Bound(BnB) maximum clique problem(MCP) parallel search

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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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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

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

ISBN: (纸本)9798350325706

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. © 2023 IEEE.

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Time-Series Embeddings from Language Models:A Tool for Wind Direction Nowcasting

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Journal of Meteorological Research 2024年第3期38卷 558-569页

作者： Decio ALVES Fabio MENDONCA Sheikh Shanawaz MOSTAFA Fernando MORGADO-DIAS University of Madeira Campus Universitário da Penteada9020-105FunchalPortugal Interactive Technologies Institute/Laboratory for Robotics and Engineering Systems and Agência Regional para o Desenvolvimento da Investigação Tecnologia e InovaçãoEdif.Madeira TecnopoloCaminho da Penteada piso-29020-105FunchalPortugal

Wind direction nowcasting is crucial in various sectors,particularly for ensuring aviation operations and *** this context,the TELMo(Time-series Embeddings from Language Models)model,a sophisticated deep learning architecture,has been introduced in this work for enhanced wind-direction *** by using three years of data from multiple stations in the complex terrain of an international airport,TELMo incorporates the horizontal u(east-west)and v(north-south)wind components to significantly reduce forecasting *** a day with high wind direction variability,TELMo achieved mean absolute error values of 5.66 for 2-min,10.59 for 10-min,and 14.79 for 20-min forecasts,processed within a swift 9-ms/step *** degree-based analysis,in comparison,yielded lower performance,emphasizing the effectiveness of the u and v *** contrast,a Vanilla neural network,representing a shallow-learning approach,underperformed in all analyses,highlighting the superiority of deep learning methodologies in wind direction *** is an efficient model,capable of accurately forecasting wind direction for air traffic operations,with an error less than 20°in 97.49%of the predictions,aligning with recommended international *** model design enables its applicability across various geographical locations,making it a versatile tool in global aviation meteorology.

关键词： wind nowcasting wind components wind direction time series prediction deep 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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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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