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Novel Surface Design of Deployable Reflector Antenna Based on Polar Scissor Structures

Chinese Journal of Mechanical engineering 2020年第5期33卷 4-18页

作者： Pengyuan Zhao Jinguo Liu Chenchen Wu Yangmin Li Keli Chen State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of ScienceShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China

Space-deployable mechanisms can be used as supporting structures for large-diameter antennas in space *** study proposes a novel method for constructing the surface design of space reflector antennas based on polar scissor *** concurrency and deployability equations of the space scissor unit with definite surface constraints are derived using the rod and vector *** equations of the spatial transformation for space n-edge polar scissor units are summarized.A new closed-loop deployable structure,called the polar scissor deployable antenna(PSDA),is designed by combining planar polar scissor units with spatial polar scissor *** overconstrained problem is solved by releasing the curve constraint that locates at the end-point of the planar scissor *** simulation and error analysis are *** results show that the PSDA can effectively fit the paraboloid of ***,deployment experiments verify the validity and feasibility of the proposed design method,which provides a new idea for the construction of large space-reflector antennas.

关键词： Deployable structures Polar scissor unit Reflector antenna Surface design

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Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation

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Journal of Bionic engineering 2020年第6期17卷 1163-1174页

作者： Ning Li Tie Yang Yang Yang Peng Yu Xiujuan Xue Xingang Zhao Guoli Song Imad HElhajj Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Itelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Bejing 100049China Rehabilitation Center for the Disabled Shenyang 110015China Vision and Robotics Lab Department of Electrical and Computer EngineeringAmerican University of BeirutBeirut 11072020Lebanon Emerging Technologies Instinute Department of Industrial&Manufacturing Systems EngineeringUniversity of Hong KongPokfulamHong Kong 999077China

Exoskeleton robots have demonstrated the potential to rehabilitate stroke ***,poor human-machine physiological coupling causes unexpected damage to human of muscles and ***,inferior humanoid kinematics control would restrict human natural *** to deal with these problems results in bottlenecks and hinders its *** this paper,the simplified muscle model and muscle-liked kinematics model were proposed,based on which a soft wrist exoskeleton was established to realize natural human ***,we simplified the redundant muscular system related to the wrist joint from ten muscles to four,so as to realize the human-robot physiological ***,according to the above human-like musculoskeletal model,the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the *** results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3%and 35.6%,***,the humanoid motion trajectories similarity of the robot reached 91.5%.Stroke patients could recover 90.3%of natural motion ability to satisfy for most daily *** work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.

关键词： stroke rehabilitation simplified muscle model soft wrist exoskeleton robot bionic kinematics model

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MRI-guided robot intervention—current state-of-the-art and new challenges

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Med-X 2023年第1期1卷 141-181页

作者： Shaoping Huang Chuqian Lou Ying Zhou Zhao He Xuejun Jin Yuan Feng Anzhu Gao Guang-Zhong Yang Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy(NERC-AMRT) Shanghai Jiao Tong UniversityShanghai 200240China Materials and Technology Center of Robotics EmpaDübendorf 8600Switzerland State Key Lab of Metal Matrix Composites and School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute of Medical Robotics and Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China The Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China

Magnetic Resonance Imaging(MRI)is now a widely used modality for providing multimodal,high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing *** addition to its diagnostic potential,its future theranostic value lies in its ability to provide MRI-guided robot intervention with combined structural and functional mapping,as well as integrated instrument localization,target recognition,and in situ,in vivo monitoring of the therapeutic *** of current applications include neurosurgery,breast biopsy,cardiovascular intervention,prostate biopsy and *** applications in targeted drug delivery and MRI-guided chemoembolization are also being *** promising progress has been made in recent years,there are still significant basic science research and engineering *** paper provides a comprehensive review of the current state-of-the-art in MRI-guided robot intervention and allied technologies in actuation,sensing,new materials,interventional instruments,and interactive/real-time *** future research directions and new clinical developments are also discussed.

关键词： MRI-guided robot intervention Actuation Sensing Materials Interventional instruments Interactive/realtime MRI

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Safe-Planner: A Single-Outcome Replanner for Computing Strong Cyclic Policies in Fully Observable Non-Deterministic Domains

Safe-Planner: A Single-Outcome Replanner for Computing Stron...

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International Conference on Advanced robotics (ICAR)

作者： Vahid Mokhtari Ajay Suresha Sathya Nikolaos Tsiogkas Wilm Decré Division Robotics Automation and Mechatronics (RAM) and Flanders MakeDepartment of Mechanical EngineeringKU LeuvenBelgium

ISBN: (纸本)9781665436854

Replanners are efficient methods for solving nondeterministic planning problems. Despite showing good scalability, existing replanners often fail to solve problems involving a large number of misleading plans, i.e., plans that do not lead to strong solutions, however, due to their minimal lengths, are likely to be found at every replanning iteration. The poor performance of replanners in such problems is due to their all-outcome determinization. That is, when compiling from non-deterministic to classical, they include all compiled classical operators in a single deterministic domain which leads replanners to continually generate misleading plans. We introduce an offline replanner, called Safe-Planner (SP), that relies on a single-outcome determinization to compile a nondeterministic domain into a set of classical domains, and ordering heuristics for ranking the obtained classical domains. The proposed single-outcome determinization and the heuristics allow for alternating between different classical domains. We show experimentally that SP avoids generating misleading plans, but rather generates weak plans that directly lead to strong solutions. The experiments show that SP outperforms state-of-the-art non-deterministic solvers by solving a broader range of problems. We also validate the practical utility of SP in real-world non-deterministic robotic tasks.

关键词： Scalability Lead Planning Task analysis Robots

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Testing and validation of a custom CAD tool to support design for manufacturing: An experimental study 41

Testing and validation of a custom CAD tool to support desig...

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41st Computers and Information in engineering Conference, CIE 2021, Held as Part of the ASME 2021 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2021

作者： Patel, Apurva Summers, Joshua D. Patel, Akash Mathieson, James L. Sbarra, Michael P. Ortiz, Joshua Clemson University ClemsonSC29634-0921 United States Department Head Mechanical Engineering University of Texas at Dallas RichardsonTX United States Automation/AI Lockheed Martin Space Autonomy and Robotics Lockheed Martin Space

ISBN: (纸本)9780791885376

While fundamentals of DFMA are widely accepted and used in the engineering design community, many CAD environments lack tools that address manufacturing concerns and provide rapid feedback to designers about manufacturing impacts of their design choices. This paper presents an experiment-based testing and validation of a rapid feedback tool that provides users a history-based prediction of manufacturing time based on the current state of the design. A between-subjects experiment is designed to evaluate the impact of the tool on design outcomes based on modeling time, part mass, and manufacturing time. Participants in the study included mechanical engineering graduate and undergraduate students with at least one semester of experience using SolidWorks. The experiment included three different design activities and three different conditions of the design tool. Participants completed up to three sessions with different experimental conditions. Analysis of the data collected shows that use of the design tool results in a small but non-significant increase in modeling time. Moreover, use of the tool results in reduced part mass on average, as well as in a within-subject comparison. Tool use reduced manufacturing time in open ended activities, but increased manufacturing time when activities focus more on mass-reduction. Participant feedback suggests that the tool helped guide their material removal actions by showing the impact on manufacturing time. Finally, potential improvements and future expansions of the tool are discussed. Copyright © 2021 by ASME

关键词： Computer aided design

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AI Micro Motion Sensors for Screening Sarcopenia-prone Elderly Subjects

AI Micro Motion Sensors for Screening Sarcopenia-prone Elder...

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International Conference on Micro/Nano Sensors for AI, Healthcare, and robotics (NSENS)

作者： Keer Wang Hongyu Zhang Clio Yuen Man Cheng Meng Chen King Wai Chiu Lai Calvin Kalun Or Yong Hu Arul Lenus Roy Vellaisamy Cindy Lo Kuen Lam Ning Xi Vivian Weiqun Lou Wen Jung Li Department of Mechanical Engineering City University of Hong Kong China Department of Social Work and Social Administration Sau Po Centre on Ageing The University of Hong Kong China Department of Biomedical Engineering Centre for Robotics and Automation City University of Hong Kong China Department of Industrial and Manufacturing System Engineering The University of Hong Kong China Department of Orthpedics & Traumatology Li Ka-Shing Faculty of Medicine The University of Hong Kong China Department of Science and Engineering City University of Hong Kong China Department of Family Medicine and Primary Care Li Ka-Shing Faculty of Medicine The University of Hong Kong China

ISBN: (数字)9798350375213

ISBN: (纸本)9798350375220

Sarcopenia, a condition characterized by age-related muscle mass and function decline, poses significant risks including falls, fractures, gait disorders, and even mortality. This study aimed to develop an AI motion sensor system utilizing micro inertial measurement units (μIMUs) to screen sarcopeniaprone elderly subjects. Subjects within the age range of 65–84 years performed single sit-to-stand and 5-time chair stand while wearing two μIMUs. K-Nearest-Neighbours (KNN) algorithms were employed to collect and analyze motion data from the tests. The 53 subjects were categorized as either healthy or sarcopeniaprone, with the sarcopenia-prone group further classified into three levels based on their condition severity. The highest classification accuracy achieved was 94.64% for distinguishing between healthy and sarcopenia-prone subjects, and 90.44% for differentiating various sarcopenia-prone risk levels. This AI motion sensor system demonstrates potential as a cost-effective and accessible approach for large-scale sarcopenia screening. Further refinement of this method could enable remote health monitoring and telerehabilitation programs catering to older adults.

关键词： Legged locomotion Measurement units Medical services Muscles Robot sensing systems Motion detection Sensors

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Fuzzy-depth objects grasping based on FSG algorithm and a soft robotic hand

arXiv

引用

arXiv 2021年

作者： Cao, Hanwen Huang, Junda Li, Yichuan Zhou, Jianshu Liu, Yunhui T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese University of Hong Kong China

Autonomous grasping is an important factor for robots physically interacting with the environment and executing versatile tasks. However, a universally applicable, cost-effective, and rapidly deployable autonomous grasping approach is still limited by those target objects with fuzzy-depth information. Examples are transparent, specular, flat, and small objects whose depth is difficult to be accurately sensed. In this work, we present a solution to those fuzzy-depth objects. The framework of our approach includes two major components: one is a soft robotic hand and the other one is a Fuzzy-depth Soft Grasping (FSG) algorithm. The soft hand is replaceable for most existing soft hands/grippers with body compliance. FSG algorithm exploits both RGB and depth images to predict grasps while not trying to reconstruct the whole scene. Two grasping primitives are designed to further increase robustness. The proposed method outperforms reference baselines in unseen fuzzy-depth objects grasping experiments (84% success rate). Copyright © 2021, The Authors. All rights reserved.

关键词： Cost effectiveness

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ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding

ASI-Seg: Audio-Driven Surgical Instrument Segmentation with ...

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

作者： Zhen Chen Zongming Zhang Wenwu Guo Xingjian Luo Long Bai Jinlin Wu Hongliang Ren Hongbin Liu Centre for Artificial Intelligence and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China Department of Electronic Engineering Chinese University of Hong Kong China State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Surgical instrument segmentation is crucial in surgical scene understanding, thereby facilitating surgical safety. Existing algorithms directly detected all instruments of predefined categories in the input image, lacking the capability to segment specific instruments according to the surgeon’s intention. During different stages of surgery, surgeons exhibit varying preferences and focus toward different surgical instruments. Therefore, an instrument segmentation algorithm that adheres to the surgeon’s intention can minimize distractions from irrelevant instruments and assist surgeons to a great extent. The recent Segment Anything Model (SAM) reveals the capability to segment objects following prompts, but the manual annotations for prompts are impractical during the surgery. To address these limitations in operating rooms, we propose an audio-driven surgical instrument segmentation framework, named ASI-Seg, to accurately segment the required surgical instruments by parsing the audio commands of surgeons. Specifically, we propose an intention-oriented multimodal fusion to interpret the segmentation intention from audio commands and retrieve relevant instrument details to facilitate segmentation. Moreover, to guide our ASI-Seg segment of the required surgical instruments, we devise a contrastive learning prompt encoder to effectively distinguish the required instruments from the irrelevant ones. Therefore, our ASI-Seg promotes the workflow in the operating rooms, thereby providing targeted support and reducing the cognitive load on surgeons. Extensive experiments are performed to validate the ASI-Seg framework, which reveals remarkable advantages over classical state-of-the-art and medical SAMs in both semantic segmentation and intention-oriented segmentation. The source code is available at https://***/Zonmgin-Zhang/ASI-Seg.

关键词： Instruments Semantic segmentation Source coding Surgery Contrastive learning Manuals Cognitive load Safety Intelligent robots Biomedical imaging

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IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction

arXiv

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arXiv 2023年

作者： Yang, Jing Chen, Yuehai Du, Shaoyi Chen, Badong Principe, Jose C. School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong Xi'An710049 China Institute of Articial Intelligence and Robotics College of Articial Intelligence Xi'an Jiaotong University Shanxi Xi'An710049 China The Computational NeuroEngineering Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module including this data-driven mechanism is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance. © 2023, CC BY-NC-ND.

关键词： Long short-term memory

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Mechanical and tribological behavioral evaluation of AA7075/TiB 2 composite processed through UST

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Materials Today: Proceedings 2021年 44卷 2492-2500页

作者： I.G. Siddhalingeshwar G.U. Raju K.G. Kodancha Vinod Kumar V Meti School of Mechanical Engineering KLE Technological University Hubballi 580031 India Automation & Robotics Department KLE Technological University Hubballi 580031 India

Development of materials with high strength having lightweight, has been the subject of intense research for the past three decades, and these are being exploited for a range of commercial applications, particularly the aviation and automotive sector. Aluminum-based metal matrix composites (AMCs) are being preferred for most of the applications. In this investigation, an attempt has been made to synthesize AA7075/TiB 2 composite through the in-situ salt melt reaction technique and ultrasound-assisted in-situ technique (UST). Furthermore, the effect of hot forging on the in-situ and also UST composites is investigated. Microstructural analysis reveals that the UST reduced the porosity level, and further, the UST refines the size of TiB 2 particles along with improvement in the dispersion of the TiB 2 particles. The assessment of the composite confirms that the UST is found to improve the mechanical and tribological characteristics significantly. UST forged composite shows enhanced hardness and strength as compared to as-cast, in-situ, and UST.

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