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arXiv 2024年

作者： Deng, Tianchen Wang, Nailin Wang, Chongdi Yuan, Shenghai Wang, Jingchuan Wang, Danwei Chen, Weidong The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: (a) inaccurate depth input. Accurate depth input is impossible to get in real-world large-scale scenes. (b) inaccurate pose estimation. Existing methods are not robust enough with the growth of cumulative errors in large scenes and long sequence. (c) insufficient scene representation capability. A single global radiance field lacks the capacity to effectively scale to large-scale scenes. To this end, we propose an incremental joint learning framework, which can achieve accurate depth, pose estimation, and large-scale scene reconstruction. A vision transformer-based network is adopted as the backbone to enhance performance in scale information estimation. For pose estimation, a feature-metric bundle adjustment (FBA) method is designed for accurate and robust camera tracking in large-scale scenes. In terms of implicit scene representation, we propose an incremental scene representation method to construct the entire large-scale scene as multiple local radiance fields to enhance the scalability of 3D scene representation. Extended experiments have been conducted to demonstrate the effectiveness and accuracy of our method in depth estimation, pose estimation, and large-scale scene reconstruction. The code will be open-sourced upon paper acceptance. © 2024, CC BY-NC-SA.

关键词： Depth perception

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IMU-Based Prediction of Multiple Grasping Gesture Intentions for Enhanced Functional Electrical Stimulation control*

IMU-Based Prediction of Multiple Grasping Gesture Intentions...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Guotao Gou Kong Hoi Cheng Jinxin Sun Chengyu Lin Wei Pan Guojing Huang Yuquan Leng Yixuan Guo Chenglong Fu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Guangxi Human Physiological Information Non Invasive Detection Engineering Technology Research Center School of Life and Environmental Sciences Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Stroke survivors frequently face the challenge of regaining hand-grasping functions, which are crucial for performing daily activities. While Functional Electrical Stimulation (FES) has demonstrated promise for hand function recovery, the application of online control methods tailored to varied daily grasps remains underexplored. In response, our study presents a refined framework utilizing an inertial measurement unit (IMU) for the real-time recognition of grasp intentions in stroke patients. Data on arm trajectories and orientations preceding diverse grasping actions were gathered from three healthy individuals, segmented by acceleration to determine high-accuracy intervals for grasp prediction. The Support Vector Classification (SVC) model emerged as a superior method for intent recognition. By segmenting data based on acceleration, our model’s general accuracy improved. Moreover, the time required to achieve a stable accuracy rate exceeding 95% was reduced from 0.8 s to 0.2 s. This research lays the groundwork for future advancements in online gesture recognition systems. The successful implementation of FES based on SVC-model predictions mark a significant step toward intuitive and adaptive rehabilitation therapies for stroke patients.

关键词： Accuracy Static VAr compensators Grasping Stroke (medical condition) Real-time systems Iron Data models Trajectory Neuromuscular stimulation Context modeling

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Flexible Multi-Channel Electrical Stimulation System for Assisting Grasping in Patients with Hemiplegia

Flexible Multi-Channel Electrical Stimulation System for Ass...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Jinxin Sun Guojing Huang Chengyu Lin Wei Pan Kong Hoi Cheng Guotao Gou Shuyan Huang Yuquan Leng Chenglong Fu Zhencheng Chen Guangxi Human Physiological Information Non Invasive Detection Engineering Technology Research Center School of Life and Environmental Sciences Guilin University of Electronic Technology Guilin China Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Stroke patients are unable to perform gripping actions effectively as normal individuals due to impaired hand function. Electrical stimulation is currently a relatively effective rehabilitation method, but there is still room for improvement in aspects such as grip control precision and grip posture selection. Therefore, we propose a multi-channel electrical stimulation system to achieve precise control of hand gripping in stroke patients and assist in hand function rehabilitation. The pressure-controlled constant current source electrical stimulation system is proposed and a flexible wearable 16-channel hand FPC electrical stimulation patch is designed according to the distribution of hand muscles. The MCU controls an external DAC chip to generate voltage to drive the pressure-controlled constant current source, thereby generating adjustable biphasic symmetrical stimulation pulse waveforms. The FPC transmits the generated stimulation pulses to the corresponding stimulation points to achieve precise electrical stimulation of specific points. Through three comparative experiments, the results indicate that precise control of finger movement angles has been achieved by adjusting the stimulation current and frequency. The flexible multi-channel hand electrical stimulation system offers a solution for restoring motor function in patients with hemiplegia.

关键词： Mechatronics Thumb Electrical stimulation Bending Stroke (medical condition) Muscles Motors Voltage control Frequency control Robots

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Improving Referring Expression Comprehension by Suppressing Expression-unrelated Proposals 3

Improving Referring Expression Comprehension by Suppressing ...

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3rd International Conference on electronic Information engineering and Computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

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Non-cooperative Stochastic Target Encirclement by Anti-synchronization control via Range-only Measurement

Non-cooperative Stochastic Target Encirclement by Anti-synch...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Fen Liu Shenghai Yuan Wei Meng Rong Su Lihua Xie Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology Guangzhou the Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper investigates the stochastic moving target encirclement problem in a realistic setting. In contrast to typical assumptions in related works, the target in our work is non-cooperative and capable of escaping the circle containment by boosting its speed to maximum for a short duration. In extreme conditions, where GPS signals are not available, weight restrictions are present, and ground guidance is absent, the agents can rely solely on their onboard single-modality perception tools to measure the distances to the target. The distance measurement allows for creating a position estimator by providing a target position-dependent variable. Furthermore, the construction of the unique distributed anti-synchronization controller (DASC) can guarantee that the two agents track and encircle the target swiftly. The convergence of the estimator and controller is rigorously evaluated using the Lyapunov technique. A real-world UAV-based experiment is conducted to illustrate the performance of the proposed methodology in addition to a simulated Matlab numerical sample. Our video demonstration can be found in the URL https://***/EDVLvP-bk8M.

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A Cyclic Small Phase Theorem for MIMO LTI Systems*

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IFAC-PapersOnLine 2023年第2期56卷 1883-1888页

作者： Chao Chen Wei Chen Di Zhao Jianqi Chen Li Qiu Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Mechanics and Engineering Science & State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Control Science and Engineering & Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between -π and π. The proposed theorem complements the celebrated small gain theorem.

关键词： Small phase theorem segmental phase cyclic feedback systems stability analysis lead-lag compensation

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Phase-Based Quantification of Sports Performance Metrics Using a Smart IoT Sensor

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IEEE Internet of Things Journal 2023年第18期10卷 15900-15911页

作者： Chen, Meng Szu, Hui Fang Lin, Hsin Yen Liu, Yifan Chan, Ho-Yin Wang, Yufan Zhao, Yuliang Zhang, Guanglie Yao, Jeffrey Da-Jeng Li, Wen Jung National Tsing Hua University Physical Education Office Hsinchu City300 Taiwan Kla Corporation Tainan City744 Taiwan Michigan State University Department of Electronic and Computer Engineering East LansingMI48824 United States Shanghai Jiao Tong University School of Mechanical Engineering Department of Industrial Engineering and Management Shanghai200240 China Northeastern University at Qinhuangdao Hebei Key Lab. of Micro-Nano Prec. Optical Sensing and Msrmt. Technol. Sch. of Control Engineering Qinhuangdao066004 China City University of Hong Kong Department of Mechanical Engineering Kowloon Tong Hong Kong City University of Hong Kong Shenzhen Research Institute CASŰCityU Joint Laboratory for Robotics Shenzhen518057 China National Tsing Hua University Department of Power Mechanical Engineering Hsinchu City300 Taiwan

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes' sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players' serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player's skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes' training. © 2014 IEEE.

关键词： Internet of things

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GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping

arXiv

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arXiv 2023年

作者： Tang, Chao Huang, Dehao Ge, Wenqi Liu, Weiyu Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. Our code, data, appendix, and video are publicly available at https://***/view/graspgpt. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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Type Synthesis of Single-DOF Multi-Loop-Coupled Planar Continuum Morphing Mechanisms

Type Synthesis of Single-DOF Multi-Loop-Coupled Planar Conti...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Jieyu Wang Yuxiang Li Yingzhong Tian Fengfeng Xi Guangbo Hao Yinjun Zhao Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Aerospace Engineering Toronto Metropolitan University Toronto ON Canada School of Engineering Electrical and Electronic Engineering University College Cork Cork Ireland School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

This paper investigates a single degree-of-freedom (DOF) multi-loop-coupled planar continuum morphing mechanism (MPCMM), which can be used in broad application prospects in fields such as robotics, aerospace, and biomedical devices. The mechanism is proposed based on the generalized kinematic chains. The design process mainly includes determining the design requirements, selecting the generalized kinematic chains based on the design requirements, modifying the chains, and choosing the most appropriate chain to construct MPCMM. In addition, the vector equation method is used to analyze the mechanisms. Finally, the MPCMM is applied to a variable camber wing trailing edge.

关键词： Couplings Kinematics Vectors Robots

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Quadrotor Autonomous Landing on Moving Platform

Quadrotor Autonomous Landing on Moving Platform

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2022 International Symposium on Biomimetic Intelligence and robotics, ISBIR 2022

作者： Wang, Pengyu Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China School of Control Science and Engineering Shandong University Jinan China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Hong Kong

This paper introduces a quadrotor's autonomous take-off and landing system on a moving platform. The designed system addresses three challenging problems: fast pose estimation, restricted external localization, and effective obstacle avoidance. Specifically, first, we design a landing recognition and positioning system based on the AruCo marker to help the quadrotor quickly calculate the relative pose;second, we leverage a gradient-based local motion planner to generate collision-free reference trajectories rapidly for the quadrotor;third, we build an autonomous state machine that enables the quadrotor to complete its take-off, tracking and landing tasks in full autonomy;finally, we conduct experiments in simulated, real-world indoor and outdoor environments to verify the system's effectiveness and demonstrate its potential. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and robotics.

关键词： Motion planning

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