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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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Mechanical Properties Inside Origami-Inspired Structures: An Overview

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Applied Mechanics Reviews 2025年第1期77卷 011001页

作者： Yan, Peng Huang, Hailin Meloni, Marco Li, Bing Cai, Jianguo Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518052 China Harbin Institute of Technology Shenzhen518052 China Key Laboratory of C & PC Structures of Ministry of Education National Prestress Engineering Research Center Southeast University Nanjing211189 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518052 China Key University Laboratory of Mechanism Machine Theory and Intelligent Unmanned Systems of Guangdong Habin Institute of Technology Shenzhen518052 China

In recent decades, origami has transitioned from a traditional art form into a systematic field of scientific inquiry, characterized by attributes such as high foldability, lightweight frameworks, diverse deformation modes, and limited degrees-of-freedom. Despite the abundant literature on smart materials, actuation methods, design principles, and manufacturing techniques, comprehensive reviews focusing on the mechanical properties of origami-inspired structures are relatively rare and unsystematic. This review aims to fill this void by analyzing and summarizing the significant studies conducted on the mechanical properties of origami-inspired structures from 2013 to 2023. We begin with an overview that includes essential definitions of origami, classical origami patterns, and their associated tessellated or stacked structures. Following this, we delve into the principal dynamic modeling method for origami and conduct an in-depth analysis of the key mechanical properties of origami-inspired structures. These properties include tunable stiffness, bistability and multistability, metamechanical properties demonstrated by origami-based metamaterials, and bio-inspired mechanical properties. Finally, we conclude with a comprehensive summary that discusses the current challenges and future directions in the field of origami-inspired structures. Our review provides a thorough synthesis of both the mechanical properties and practical applications of origami-inspired structures, aiming to serve as a reference and stimulate further research. © 2025 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Degrees of freedom (mechanics)

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FT-TF: A 4D Long-Term Flight Trajectory Prediction Method Based on Transformer 42

FT-TF: A 4D Long-Term Flight Trajectory Prediction Method Ba...

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42nd Chinese Control Conference, CCC 2023

作者： Luo, Shijin Zhao, Minghui Zhao, Zhenjie Li, Lun Zhang, Shiyong Zhang, Xuebo Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China

ISBN: (纸本)9789887581543

The accurate prediction of target aircraft trajectory in the process of air combat can significantly improve the ability of aircraft to gain air superiority. Most of the trajectory prediction methods currently applied in air combat are based on traditional time-series prediction algorithms such as LSTM with short prediction steps, which can not realize the demand for long-term prediction in air combat. To address the issue, a novel prediction method FT-TF is proposed in this paper. FT-TF is a multi-step 4D flight trajectory prediction method based on Transformer applied to further improve the accuracy and practicality of trajectory prediction problems. FT-TF uses the first 30 steps of aircraft historical trajectory data as input and the last 30 steps as output. Flight trajectory data sets of aircraft in air combat are used in the experiment to make the trained model closer to the real air combat scene. The experimental results show that the proposed FT-TF can achieve higher prediction accuracy than LSTM and BP neural network. Related Transformer variants of the algorithm are also added to the experiment for comparison, the results demonstrate that the proposed method achieves the smallest prediction error. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Aircraft

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Reliable Observer-Based Control Against Sensor Failures for Fractional Order systems 42

Reliable Observer-Based Control Against Sensor Failures for ...

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42nd Chinese Control Conference, CCC 2023

作者： Li, Bingxin Zhang, Yujie Liu, Yaowei Zhao, Xin Nankai University Institute of Robotics and Automatic Information System The Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

ISBN: (纸本)9789887581543

In the paper, stability analysis of a class of incommensurate fractional-order systems (IFOS) is studied. These sufficient conditions are based on the linear matrix inequality (LMI) and straightforwardly used to solve the feasible solutions with the LMI toolbox. Finally, two examples including a circuit example are given to illustrate the effectiveness of the proposed method, and the comparison with the previous results shows that the proposed method is less conservative. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Linear matrix inequalities

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A Distributed Task Allocation Algorithm for UAV Swarm Based on Local Information 4

A Distributed Task Allocation Algorithm for UAV Swarm Based ...

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4th International Symposium on Artificial Intelligence and intelligent Manufacturing, AIIM 2024

作者： Zhang, Junfu Liu, Chengjin Liu, Tao Jiang, Shiqi School of Mechanical Engineering Xi Hua University Chengdu China International Joint Institute of Robotics and Intelligent System School of Automation Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541729

This article proposes a distributed task allocation algorithm based on local information for unmanned aerial vehicle (UAV) swarm. A Part of the UAVs which are in the information exchange range participate in the local range auction. The auction members will change due to the movement of the UAVs in the following auctions. Each UAV in the swarm will be allocated a task by multiple rounds of local range auctions. The experimental data clearly demonstrate that the method has superior performance for enabling UAV swarm to complete the allocation of tasks based on local information. © 2024 IEEE.

关键词： Consensus algorithm

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A Self-organized Coverage Algorithm for UAV Swarms based on Color Information Recognition 4

A Self-organized Coverage Algorithm for UAV Swarms based on ...

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4th International Symposium on Artificial Intelligence and intelligent Manufacturing, AIIM 2024

作者： Liu, Tao Zhang, Xiao Zhang, Junfu Jiang, Shiqi School of Mechanical Engineering Xi Hua University Chengdu China International Joint Institute of Robotics and Intelligent System School of Automation Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541729

This paper proposes a self-organized coverage method for unmanned aerial vehicle (UAV) swarm aimed at multi-area coverage. The proposed method attempts to take the color light emitted from the UVA as an important state information. The virtual forces and the color state are incorporated to develop the self-organized coverage algorithm, so that the communication dependency among UAV swarm has been weakened. Simulation experiments show that under the condition of weak communication, the algorithm effectively achieves coverage of multiple areas. Moreover, when part of UAVs in the swarm are damaged or fails, the swarm based on the proposed algorithm can achieve self-organized recovery and continue to complete the task. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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G²VD Planner: Efficient Motion Planning With Grid-Based Generalized Voronoi Diagrams

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IEEE Transactions on Automation Science and Engineering 2024年 22卷 3743-3755页

作者： Jian Wen Xuebo Zhang Qingchen Bi Hui Liu Jing Yuan Yongchun Fang Institute of Robotics and Automatic Information System College of Artificial Intelligence and Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

In this paper, an efficient motion planning approach with grid-based generalized Voronoi diagrams (G2VD) is newly proposed for mobile robots. Different from existing approaches, the novelty of this work is twofold: 1) a new state lattice-based path searching approach is proposed, in which the search space is reduced to a novel Voronoi corridor to further improve the search efficiency; 2) an efficient quadratic programming-based path smoothing approach is presented, wherein the clearance to obstacles is considered to improve the path clearance of hard-constrained path smoothing approaches. We validate the efficiency and smoothness of our approach in various challenging simulation scenarios and outdoor environments. It is shown that the computational efficiency is improved by 17.1% in the path searching stage, and path smoothing with the proposed approach is 6.6 times faster than an advanced sparse-banded structure-based path smoothing approach and 53.3 times faster than the popular timed-elastic-band planner. A video showing outdoor navigation on our campus is available at https://***/iMXGthgvp58 . Note to Practitioners—This paper is motivated by the challenges of motion planning problems of mobile robots. An efficient motion planning approach called G2VD planner is proposed by combining path searching, path smoothing, and time-optimal velocity planning. Extensive simulation and experimental results show the effectiveness of the proposed motion planning approach. However, the prediction information of dynamic obstacles is not incorporated in the proposed motion planner, thus the motion planner may be a bit sluggish in response to dynamic obstacles. Furthermore, we plan to integrate the intention/trajectory prediction of pedestrians/vehicles into the proposed framework to enhance the foreseeability of the motion planner.

关键词： Smoothing methods Planning Robots Search problems Mobile robots Safety Heuristic algorithms

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Learning Compliant Assembly Strategy From Demonstration

Learning Compliant Assembly Strategy From Demonstration

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Liu, Sheng Sheng, Juyi Ou, Yongsheng China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China

ISBN: (纸本)9798350327182

Compared with robots, humans can complete the different assembly tasks of parts flexibly and quickly. By teaching robots with human experiences, not only the industrial assembling tasks can be resolved, but also many other robot applications can be realized. The pose adjustment stage is the most critical part of the peg-in-hole assembly process. This paper analyzes the contact force in the pose adjustment stage and calculates the control variables that affect the assembly motion. Based on the Gaussian mixture model (GMM), the nonlinear mapping relationship between the control variables and the state variables during the assembly is established using the human demonstration data, and the parameters of the model are solved by the expectation maximization (EM) algorithm, and thus the human-like compliant assembly is completed by a robot. In order to verify the effectiveness of the demonstration-learning algorithm, a peg-in-hole assembly experiment was carried out using KUKA manipulator. Finally, the experiment shows that the proposed learning-based method not only improves the efficiency of the robot peg-in-hole assembly but also makes the manipulator have a satisfied adaptive ability to the complex environments in the assembly process. © 2023 IEEE.

关键词： Manipulators

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Reinforcement Learning Methods for Fixed-Wing Aircraft Control 9

Reinforcement Learning Methods for Fixed-Wing Aircraft Contr...

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9th International Conference on systems and Informatics, ICSAI 2023

作者： Li, Lun Zhang, Xuebo Wang, Yang Qian, Chenxu Zhao, Minghui Nankai University Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Tianjin China Tianjin Municipal Data Management Center Tianjin China

ISBN: (纸本)9798350383706

In recent years, reinforcement learning (RL) has been widely researched for fixed-wing aircraft control, offering transformative potential for more adaptive, efficient, and autonomous flight operations. This paper presents a comprehensive analysis of the current state, advantages, challenges, and future prospects of employing RL in fixed-wing aircraft control. The advantages of RL include model-free, adaptive decision-making, and effectively solving complex nonlinear constraints, leading to increased efficiency and maneuverability in high-dynamic environments. However, significant challenges, such as the guarantee of stability and optimization, interpretability, and sim-to-real transferring, highlight areas necessitating further attention and development. Overcoming these challenges requires collaborative efforts among aviation experts, control theory experts, and machine learning researchers to ensure safe, reliable integration of RL in flight systems. As research and advancements continue, the RL holds promise for providing a more efficient, maneuverable, and autonomous strategy for controlling fixed-wing aircraft in the future. © 2023 IEEE.

关键词： Reinforcement learning

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TGCN-P: A TCN-GCN Network With Weighted Graph Constructed by Pearson Correlation Coefficient for Human Motion Tracking

TGCN-P: A TCN-GCN Network With Weighted Graph Constructed by...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Li, Xiaoyu Wang, Jingnan Huang, Binhua Liu, Chengliang Liu, Yiwen Zhang, Yupo Yi, Zhengkun Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch Shenzhen518055 China

ISBN: (纸本)9798350327182

Neural networks based on graph convolution are simple and effective network structures that have better performance in some fields compared to traditional convolutional networks. However, when graph convolution networks (GCN) are applied to other fields, how to transform data or features into graph edges is a challenge needed to be studied. In previous studies of non-Euclidean spatial data such as relationship studies, people achieve graph construction by indicating the connection or not of nodes with binary values. However, when graph network is applied to some multi-sensor data scenarios, especially human motion tracking tasks, binarization is obviously not the best solution for the weights of edges. In addition, relying on manual graph construction may ignore some cross-space sensor relationships, which makes the update information of nodes far away slower or unavailable. Therefore, we try to use Pearson correlation coefficient (PCC) as the estimation of node correlation, and construct a complete graph to enhance the connectivity of nodes, which achieve faster signal transmission at remote nodes. After combining it with the temporal convolutional network (TCN), we propose a new network structure TGCN-P. Experimentally, this approach is more effective compared to other baseline models. © 2023 IEEE.

关键词： Convolution

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