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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Shipeng Lv Yuntian Zhao Zhenyang Chen Chengyuan Gao Longteng Hu Zhenzhong Jia Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology (SUSTech) Shenzhen China

ISBN: (纸本)9781665431545

Changing the center of mass (COM) by adjusting rover’s posture is a commonly used design and control strategy to improve its mobility over sandy slopes. Different from traditional passive/active suspension system and wheel-leg designs, we use a novel rover design for improved sloped mobility over soft terrains. Our rovers consist of a mobile base (with differential suspension), a main body, and a 2-degree-of-freedom (2-DOF) body-rotating mechanism that can change the pose of the main body relative to the mobile base. By taking full advantage of the main body weight which accounts for most of the robot weight, we can effectively adjust the rover COM and load distributions for improved mobility. Based on mechanics analysis, the wheel-load model and terramechanics model, we propose a control strategy for slope climbing and traversing tasks. When given input information such as slope angle and travel direction, the control algorithm can adjust the rover posture for optimal mobility over slopes. Experimental results indicate that the proposed design and control strategy can effectively improve the rover mobility performance (improved traction, reduced slippages) over soft sloped terrains.

关键词： Analytical models Mechatronics Machine vision Mobile robots Task analysis Load modeling

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Liu, Yu Zhang, Yuexin Li, Kunming Qiao, Yongliang Worrall, Stewart Li, You-Fu Kong, He Shenzhen518055 China Department of Mechanical Engineering City University of Hong Kong Hong Kong School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China Australian Centre for Field Robotics The University of Sydney NSW2006 Australia Australian Institute for Machine Learning The University of Adelaide SA5005 Australia Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering crossdataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme. © 2024, CC BY.

关键词： Trajectories

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A Novel Multi-Stream Informer Used for Lower Extremity Joint Angle Estimation 12

A Novel Multi-Stream Informer Used for Lower Extremity Joint...

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12th International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Zhou, Xin Zhang, Liming Liu, Jiaqing Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In lower extremity exoskeleton rehabilitation systems, synergy and proportionality between the human lower extremity and the exoskeleton robot have been a critical goal to pursue. In recent years, changeable deep learning models based on multi-source signals have been used for lower limb continuous motion estimation. During the process, multimode signals are fed into the time processing model as time series. However, there are some deficiencies in this framework, on the one hand, what kind of multi-source combination can be more efficient;on the other hand, most of the existing models need several iterations or network stacking to complete the extraction of temporal information. This study set out to address the above problems, we put forward Multi-Stream Informer (MS-Informer), using Multi-Stream combination and sparse attention mechanisms for lower limb continuous motion estimation. In the experiment, we compare MS-Informer and Temporal Convolutional Network (TCN) and Long Short-term Memory (LSTM) under distinctive multi-source combinations. In summary, MS-informer performs well in multiple evaluations compared with other models. Experimental results illustrate that the MS-Informer is more efficacious in lower limb joint angle estimation and is a promising method for achieving coordinated control of lower limb exoskeletons. © 2022 IEEE.

关键词： Motion estimation

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Transformer-Based Fusion of RGB and Depth Images for Terrain Recognition

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Procedia Computer Science 2024年 250卷 22-29页

作者： Hui Chen Chunjie Chen Zhuo Wang Fangliang Yang Heng Pan Xinyu Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen 518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Shenzhen 518055 China

This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image classification, our approach utilizes both early fusion and late fusion methods to explore their impact on terrain classification. This approach achieved fast and accurate performance in recognizing terrain. We captured data for walking upstairs, downstairs, upward ramps, downward ramps and level ground and used the vision transformer for data fusion. This work compares the performance of unimodal RGB model, unimodal deep model, Pre-Fusion model and Post-Fusion model in terms of accuracy, recall and processing frequency, with accuracies of 0.954, 0.934, 0.976, and 0.976, respectively. The results show that the Post-Fusion model slightly outperforms the Pre-Fusion model in terms of accuracy and recall with more reliable classification performance. To understand the attention mechanism better, we've been visualizing the weights of the model, observing how different attention heads may capture different features of the data from various dimensional perspectives. The visualization helps in interpreting the model's focus and how it distinguishes between features of different nature, such as color texture and spatial depth. These findings can inform the ongoing refinement of terrain classification systems for applications in robotics and autonomous navigation.

关键词： Multimodal fusion terrain recognition vision transformer depth camera

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A Learning Human-Based Method For Peg-In-Hole Assembly Using ELM

A Learning Human-Based Method For Peg-In-Hole Assembly Using...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Juyi Sheng Sheng Liu Bing Zhu Chi Zhu Yongsheng Ou Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Maebashi Institute of Technology Japan Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems (#2019B121205007)

This paper focuses on using sensor information to adjust the position for assembly in the presence of uncertainty error in vision. Firstly, a human demonstration of the peg-in-hole assembly is provided, and the data is collected for the next step of training. We start with a human demonstration and collect training data. A network is trained with 6D Force/Torque sensor (F/T sensor) as input and a strategy of which direction the robot end-effector needs to move as output. An algorithm using Extreme Learning Machines (ELM) search is proposed. This is a human-like method of searching for holes. We validated the effectiveness of our method on a KUKA iiwa 7 robot arm.

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Design of magnetorheological brake with adjustable interdigitated comb for lower limb prostheses and exoskeletons *

Design of magnetorheological brake with adjustable interdigi...

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IEEE International Conference on robotics and Biomimetics

作者： Qiuyu Shi Xuan Zhao Haisu Liao Yongji Lin Ruisen Huang Xinyu Wu Fei Gao Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Shatin China

Magnetorheological (MR) fluids-based devices such as MR dampers and brakes can output a continuously controllable damping force via modulating the magnitude of the applied magnetic field. Owing to low energy consumption and fast response, recently vast MR dampers and brakes are widely studied and explored for lower limb prostheses and exoskeletons. This paper proposed a novel MR brake with adjustable interdigitated comb for lower limb prostheses. Through actively adjusting the area the of the comb engagement, the maximum viscous and field-induced damping torque can be increased or decreased. Because of that, the dynamic adjustment interval of the output damping torque can be increased to meet the function requirements of the prosthetic knee in different walking modes. Magnetic analysis via ANSYS is performed to check the magnetic density of the MR fluid under different excitations. Mathematical models of the damping torque are built. Finally, a prototype of the novel MR brake is fabricated.

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Adaptive admittance control of human-exoskeleton system using RNN optimization

Adaptive admittance control of human-exoskeleton system usin...

引用

2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Lian, Pengchen He, Yong Ma, Yue Liu, Jingshuai Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

For wearable exoskeletons, ensuring safety and stability while reducing the effort of human lower limbs during walking remains a challenge. The level of safety can be guaranteed by constraining the exoskeleton robot to never exhibit any erratic behavior. But beyond that, the cooperative motion generated in human-exoskeleton interactions should be truly intuitive and should minimize the limitations of human performance. Therefore, this paper presented a new adaptive admittance control law, which not only ensures the stability of the lower limb exoskeleton in the constrained motion but also provides a very harmonious human-exoskeleton interaction. The proposed approach is applied to the movement of the exoskeleton containing the knee joint in the swing phase. The input of the admittance control system is the human-exoskeleton interaction force measured by one single-dimensional force sensor. The performance of the method was theoretically analyzed and discussed. Experiments were conducted in Matlab simulation environment to verify the effectiveness of aid to the swinging leg. Compared with the fixed admittance control, the adaptive admittance control system optimized by a Recurrent Neural Network(RNN) greatly improves the level of interaction between the exoskeleton and the wearer. © 2021 IEEE.

关键词： Recurrent neural networks

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Overconstrained Coaxial Design of Robotic Legs with Omni-Directional Locomotion

SSRN

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SSRN 2022年

作者： Gu, Yuping Feng, Shihao Guo, Yuqin Wan, Fang Dai, Jiansheng Pan, Jia Song, Chaoyang Southern University of Science and Technology Guangdong Shenzhen China The University of Hong Kong Hong Kong King’s College London London United Kingdom Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Guangdong Shenzhen China

Overconstrained linkages are a class of spatial mechanisms with specifically designed geometrical conditions. While extensively researched, their engineering potential is yet to be explored. This paper investigates the design of overconstrained linkages as robotic legs with coaxial actuation, starting with the simplest case, Bennett linkage, to establish the theoretical foundations and engineering advantages towards a class of overconstrained robots. We proposed an alternative form design of spatial link and joint so that overconstrained linkages can be rapidly fabricated and conveniently fixed coaxially on a pair of servo actuators as a reconfigurable leg module. We adopted multi-objective optimization to refine the design parameters by analyzing its manipulability metric and force transmission, enabling omni-directional ground locomotion projected from a three-dimensional surface workspace. We further explored the kinematics and design potentials to generalize the proposed method for all overconstrained 5 R and 6 R linkages, paving the path for a future direction in overconstrained robotics. © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A smart wearable device for capturing biomechanical energy from human knee motion

A smart wearable device for capturing biomechanical energy f...

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： Hugo Hung-Tin Chan Haisu Liao Xuan Zhao Junrui Liang Wei-Hsin Liao Xinyu Wu Fei Gao Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Sustainable power supply is a challenge for portable and wearable electronic devices such as cell phones and headsets. To address this, researchers proposed capturing biomechanical energy from human motion to generate electricity. This paper proposed and developed a lightweight wearable device to capture the biomechanical energy from the human knee motion. To reduce the effect of inertial force on human gait, we developed a lightweight and compact transmission chain to convert the bidirectional rotation of the knee to a unidirectional rotation of the generator. Two input bevel gears with opposite one-way bearings on the same shaft are engaged with a single output bevel gear of the generator thereby only one input bevel gear is engaged for each input direction, achieving unidirectional output. In addition, to reduce velocity fluctuation and further minimize the effect of inertial force, a flywheel was fixed to the motor shaft via a gearbox. A prototype of the wearable device was developed and tested on a subject walking on a treadmill. Experimental results shows the flywheel enabled the harvester to achieve a continuous output while halving voltage fluctuations compared to a conventional harvester. The harvesters average power output can reach 0.11 W with minimal effects on the subject’s walking gait.

关键词： Knee Legged locomotion Biomechanics Shafts Fluctuations Voltage fluctuations Torque

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