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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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Real-time Trajectory Planning for Redundant 7-dof Space Manipulators with Unknown Link Lengths

Real-time Trajectory Planning for Redundant 7-dof Space Mani...

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robotics, Control and Automation (ICRCA), International Conference on

作者： Jian Liu Zhiqi Li Shicai Shi Minghe Jin State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Trajectory planning for redundant space manipulators in complex spatial environments is critical for ensuring successful on-orbit task execution, especially when the manipulator’s link lengths are unknown. Traditional algorithms struggle to establish the transformation between task space and joint space under such conditions, posing significant challenges to autonomous trajectory planning. This study proposes a novel trajectory planning method for a seven-degree-of-freedom redundant space manipulator with a shoulder-wrist offset. The method utilizes a trained pose control decision system to generate continuous, smooth, and singularity-free trajectories in real time, satisfying positional and orientational accuracy requirements for target capture. A neural network with strong nonlinear fitting capabilities is employed as the position control decision module for joints 1-4, trained using a "cautious exploration, greedy exploitation" strategy for efficient and safe trajectory optimization. For joints 5-7, an analytical orientation control decision module adjusts the end-effector’s attitude. Together, these modules form a pose control decision system that adheres to constraints on joint angular velocity, acceleration, and singularity avoidance. The proposed approach is validated through simulations in both static and dynamic scenarios, with simulation results confirming its effectiveness.

关键词： Accuracy Trajectory planning Simulation Position control Aerospace electronics Manipulators Control systems Real-time systems Angular velocity Manipulator dynamics

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A Study of Prototypical Network Techniques for Cross-Subject EEG Analysis 27

A Study of Prototypical Network Techniques for Cross-Subject...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Liu, Wenchao Wang, Guagnyu He, Yuhong Bo, Hongjian Ma, Lin Li, Haifeng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Faculty of Computing Harbin Institute of Technology Harbin China Shenzhen Academy of Aerospace Technology Shenzhen China

ISBN: (纸本)9781643685489

Electroencephalography (EEG), a technique that uses electrodes on the scalp to record changes in cerebral electrical potentials, plays a key role in various brain-computer interface applications. However, due to the large differences in EEG distribution across subjects, how to accurately classify cross-subject EEG is still a challenge. In practice, it is unavoidable to collect data for each subject to perform calibration, which is cumbersome and expensive. In this paper, we propose a Cross Subject EEG Prototypical Network (CS-EEGPNet) for accurate few-shot EEG classification. To the best of our knowledge, we are the first to eliminate inter-subject distribution shift in few-shot EEG classification through multiple feature layers computation. Specifically, to avoid the effect of individual differences, we propose a few-shot feature normalization (FSFN) method, which uses the mean and variance of the same subject’s support set features to normalize the query set features. Meanwhile, we design a prototypical module based on auxiliary factors. This module utilizes learnable vectors to store category-related information for assisting the construction of prototypes, thus mitigating the effects of EEG’s randomness and non-stationary characteristics on cross-subject few shot classification. To validate the effectiveness of the method, we selecte the motor imagery paradigm, which is relatively complex in brain-computer interfaces, and test our method on the BCIC IV 2a and HGD datasets. The results demonstrate that our method significantly enhances EEG classification performance, achieving the state-of-the-art accuracies. © 2024 The Authors.

关键词： Electroencephalography

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A Lower Limb Exosuit for Assisting Knee Joint in Rehabilitation During Walking

A Lower Limb Exosuit for Assisting Knee Joint in Rehabilitat...

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Mechatronics and robotics Engineering (ICMRE), International Conference on

作者： Yao Huang Haotian Ju Songhao Guo Tianjiao Zheng Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

For knee rehabilitation patients, it is difficult to maintain balance while walking, and completing a normal gait cycle is challenging. Therefore, we propose a lower limb exosuit tailored for the knee rehabilitation patients to assist in normal walking, and experimentally evaluated the assistance effect of the lower limb exosuit. We mimic the mechanism of the skeletal muscles near the knee joint, by contracting and relaxing the length of the flexible Bowden cable between the thigh and calf, generating assistive torque on the knee joint periodically. We use Inertial Measurement Unit (IMU) to measure lower limb motion parameters, identify gait cycle states, and calculate corresponding plantar pressure and knee joint torque for each phase. We compared surface electromyography (sEMG) signals of three muscle groups (biceps femoris (BF), quadriceps femoris (QF), and gastrocnemius (G)) under conditions of wearing and not wearing the exosuit. The experimental results show that compared to not wearing the exosuit, the mean normalized sEMG signals of the three muscle groups decreased by 4.61 %, 2.41 %, and 3.97% respectively while wearing it, the peak values decreased by 11.60%, 25.69%, and 14.99% respectively, with the quadriceps femoris receiving the highest degree of assistance.

关键词： Knee Legged locomotion Limbs Torque Measurement units Thigh Muscles Pressure measurement Torque measurement Robots

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A Microgravity Simulation Experimental Platform For Small Space Robots In Orbit

arXiv

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arXiv 2025年

作者： Luo, Hang Zhou, Nanlin Zhang, Haoxiang Han, Kai Zhao, Ning Yang, Zhiyuan Qi, Jian Zhao, Sikai Zhao, Jie Zhu, Yanhe State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

This study describes the development and validation of a novel microgravity experimental platform that is mainly applied to small robots such as modular self-reconfigurable robots. This platform mainly consists of an air supply system, a microporous platform and glass. By supplying air to the microporous platform to form an air film, the influence of the weight of the air foot and the ventilation hose of traditional air-float platforms on microgravity experiments is solved. The contribution of this work is to provide a platform with less external interference for microgravity simulation experiments on small robots. Copyright © 2025, The Authors. All rights reserved.

关键词： Microgravity processing

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MRI Super-Resolution via Hybrid Information Enhancement Network based on Multi-Attention and Adaptive Convolution

MRI Super-Resolution via Hybrid Information Enhancement Netw...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jixin Yu, Hongjian Du, Zhijiang Hua, Xin Li, Zibo Zhao, Hui Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin China The 6th Medical Center Chinese Pla General Hospital Senior Department of Otolaryngology Head and Neck Surgery Beijing China

ISBN: (纸本)9798350386226

Deep learning-based super-resolution (SR) reconstruction is a critical approach which is used to generate high-resolution images from corresponding low-resolution images. However, the CNN-based methods are ineffective in capturing global information, whereas the Transformer-based methods have limited ability to model long-range dependencies caused by window self-attention. Besides, it is a challenging task to recover lost high-frequency information from downsampled images. In this paper, a Hybrid Information Enhanced Network (HIEN) is proposed for MRI super-resolution task. Specifically, we propose a Spatial-Channel Hybrid Attention (SCHA) to enhance specific semantics representation by combining spatial and channel self-attention together. To recover more high-frequency components, we propose a Dynamic High-Frequency Pass Filter (DHPF) to preserve high-frequency information adaptively in pixel-wise. The results of our extensive experiments indicate that HIEN outperforms other state-of-the-art methods. © 2024 IEEE.

关键词： Image reconstruction

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Non-convexity Pricing and Allocating Costs in Stochastic Electricity Markets

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CSEE Journal of Power and Energy systems 2024年第4期10卷 1466-1477页

作者： Wei Lin Zhifang Yang Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina State Key Laboratory of Power Transmission Equipment&System Security and New Technology College of Electrical EngineeringChongqing UniversityChongqing 400030China

Stochastic electricity markets have drawn attention due to fast increase of renewable *** results in two issues:one is to reduce uplift payments arising from non-convexity under renewable uncertainties,and the other one is to allocate reserve costs based on renewable *** resolve the first issue,a convex hull pricing method for stochastic electricity markets is *** dual variables of system-wide constraints in a chance-constrained unit commitment model are shown to reduce expected uplift payments,together with developing a linear program to efficiently calculate such *** resolve the second issue,an allocation method is proposed to allocate reserve costs to each renewable power plant by explicitly investigating how renewable uncertainties of each renewable power plant affect reserve *** proposed methods are validated in a 24-period 3-unit test example and a 24-period 48-unit utility example.

关键词： Convex hull pricing cost allocation non-convexity renewable uncertainty stochastic electricity market

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Towards semi-supervised myoelectric finger motion recognition based on spatial motor units activation

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Science China(Technological Sciences) 2022年第6期65卷 1232-1242页

作者： GUO WeiChao WANG Mian SHENG XinJun ZHU XiangYang State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Meta Robotics Institute Shanghai Jiao Tong UniversityShanghai 200240China

It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUAPt) from high-density surface electromyographic(sEMG) ***,the existing EMG decoding algorithms rarely obtain the spatial matching relationship between decoded motion units(MU) and designated muscles,and the control interface can only recognize the trained hand *** this study,a semi-supervised HMI based on MU-muscle matching(MMM) is proposed to recognize individual finger motions and even the untrained combined multi-finger *** automatic channel selection from high-density s EMG signals,the optimal spatial positions to monitor the MU activation of finger muscles are *** tapping experiment is carried out on ten subjects,and the experimental results show that the proposed s EMG decomposition algorithm based on MMM can accurately identify single finger motions with an accuracy of 93.1%±1.4%,which is comparable to that of state-of-the-art pattern recognition ***,the MMM allows unsupervised recognizing the untrained combined multi-finger motions with an accuracy of 73%±3.8%.The outcomes of this study benefit the practical applications of HMI,such as controlling prosthetic hand and virtual keyboard.

关键词： myoelectric control semi-supervised motor units activation channel selection template matching

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Stage Recognition of Surface Discharge in Oil-Impregnated Paper Based on Convolutional Neural Network 4th

Stage Recognition of Surface Discharge in Oil-Impregnated Pa...

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4th International Symposium on Insulation and Discharge Computation for Power Equipment, IDCOMPU 2023

作者： Zhou, Yuanxiang Chen, Jianning Li, Yongyin State Key Laboratory of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China

ISBN: (纸本)9789819974122

The stage recognition of surface discharge in oil-impregnated paper is of great significance to the safe and reliable operating of power transformer and the construction of digital twin system. Therefore, a convolutional neural network (CNN) method is proposed in this paper to identify the development stages of surface discharge. Firstly, the surface discharge experiment of oil-paper insulation was carried out by step-up voltage method. Then, according to the difference of phase-resolved partial discharge (PRPD), the surface discharge process could be divided into various stages. Finally, CNN was adopted to identify different stages of surface discharge, compared with support vector machine (SVM) and back propagation neural network (BPNN) based on 24-dimensional feature extraction. The results show that after 100 iterations, the recognition accuracy of CNN on surface discharge stage reaches 99.17%, while the overall accuracy of SVM and BPNN is only 91.04% and 87.29%, respectively. The CNN model proposed in this paper can automatically and effectively identify the PRPD patterns of different stages of surface discharge, with a higher recognition accuracy than the traditional methods such as SVM and BPNN. © Beijing Paike Culture Commu. Co., Ltd 2024.

关键词： Support vector machines

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A Lower Limb Exosuit for Assisting Knee Joint in Rehabilitation During Walking 11

A Lower Limb Exosuit for Assisting Knee Joint in Rehabilitat...

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11th International Conference on Mechatronics and robotics Engineering, ICMRE 2025

作者： Huang, Yao Ju, Haotian Guo, Songhao Zheng, Tianjiao Zhao, Jie Zhu, Yanhe Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin China

ISBN: (纸本)9798331509293

关键词： Neuromuscular rehabilitation

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