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Predict Hip Joint Moment Using CNN for Hip Exoskeleton Control 1

引用

16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Zhang, Yuanwen Xiong, Jingfeng Qian, Yuepeng Chen, Xinxing Guo, Yixuan Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China National University of Singapore Singapore117583 Singapore

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

The assistance of the hip exoskeleton is closely related to hip joint moment, and the corresponding assistance can be generated by hip joint moment directly. However, it is difficult to obtain the hip joint moment directly from the sensor during walking. An effective method is to use deep learning models to predict hip joint moment using hip joint angle information. But there are still many challenges in applying this method to practical control systems. This study designs specific parameters to train a deep neural network for predicting hip joint moments and discusses the effects of the number of convolutional layers (N) and the length of sampling time (SL) on the prediction accuracy and the computation time in a real control system. Experiments show that for N ≤ 5, using angle information with SL ≤ 1.0 s achieves the highest prediction accuracy. However, for larger values of N (N > 5), the optimal SL becomes to be longer, where it increases to 2.0 s and the corresponding RMSE is 0.06260 Nm/kg and 0.05977 Nm/kg, respectively. These results indicate the importance of SL for prediction accuracy. Furthermore, a comparative analysis of the computation time with models using different N and SL is performed, and the relationship between computation time and prediction accuracy is discussed. This study provides specific model training parameters and detailed comparative analysis, which facilitates further application of deep learning models on hip exoskeletons. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Forecasting

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Robotic Simulator with High-Precision Perception of Contact Dynamics

Robotic Simulator with High-Precision Perception of Contact ...

引用

IEEE International Conference on Development and Learning, ICDL

作者： Yongcan Zhou Yang Pan Tianjian Lei Junpeng Chen Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems 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

A robotic simulator constitutes a pivotal tool in achieving accurate modeling and simulation within a virtual environment, especially concerning comprehensive robot training. This paper introduces a novel robotic simulator that employs a continuous compliant contact model, ensuring continuous and stable calculations during the contact process, with a specific focus on precise contact force computation during object collisions. The proposed physics engine captures energy transformations for diverse contact surface shapes and material properties, employing spring-dampers in collision detection. Dynamic equations are derived based on the spring stiffness system, damping coefficient, and external forces applied during the contact phase, enabling effective determination of dynamic parameters throughout the contact process, including position, velocity, and contact force during penetration. Experimental validation of the physics engine's contact solver involves investigating bouncing balls under varying coefficients of restitution. The conducted experiments successfully capture continuous variations in parameters such as contact force during the penetration process. The primary contribution of this paper lies in advancing physics engines to achieve accuracy in simulating and modeling virtual environments, particularly when applied to large-scale robot training. Beyond this context, the high-precision perception of contact dynamics within our robotic simulator bears potential implications in areas such as industrial automation, robotic manipulation, and virtual prototyping.

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Wheeled Mobile Robot Based on Adaptive Linear Quadratic Gaussian Control 29

Wheeled Mobile Robot Based on Adaptive Linear Quadratic Gaus...

引用

第29届中国控制与决策会议

作者： Yubin Liao Yongsheng Ou Shan Meng Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen University

ISBN: (纸本)9781509046584

To deal with the existing issues of input saturations and uncertain positioning corrupted by Gaussian noises in the tracking control of Wheeled Mobile Robot(WMR),this paper presents a framework which applies the Adaptive Linear Quadratic Gaussian(ALQG) control with Genetic Algorithm based on linear discrete *** work of ALQG includes three ***,to address the problem of input saturations,we consider a linear discrete system as the Lagrangian multiplier and saturation constraint as the inequality constraint,and the linear quadratic controller with the penalty function as the objective function is solved by the interior point ***,in the above implementation of Linear Quadratic Regulator control,the weighting matrix under different conditions may result in different tracking *** address this issue,weighting matrices of different initial poses and reference trajectories are adaptively explored by Genetic Algorithm with control performance indexes as constraint functions,and the optimal weighting matrix is final ***,concerning the uncertainty problem in positioning,the Kalman Filter algorithm with real-time measurement of the disturbed position of the posture filter is applied in real time to obtain more accurate *** ALQG simulation of the WMR is carried out in the MATLAB software platform,and the tracking quality of the ALQG is discussed *** results show that the trajectory tracking controller of wheeled mobile robot based on ALQG can track the target trajectory stably and has good anti-interference abilities.

关键词： Mobile Robot Trajectory Tracking Linear Quadratic Gaussian Control

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Different Height Stairs Climbing Pattern Generation for Humanoids

Different Height Stairs Climbing Pattern Generation for Huma...

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

作者： Yang, Ming Tian, Dingkui Xu, Minjia Li, Feng Sun, Jianquan Shang, Weiwei Wu, Xinyu University of Science and Technology of China Hefei230026 China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China Daqing Vocational College Daqing163255 China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen518055 China

ISBN: (纸本)9798350327182

Climbing stairs has been an indispensable ability for humanoids. This paper presents a novel and practical problem: climbing stairs of varying heights. In this study, the humanoid is modeled as an inverted pendulum and set the pelvis trajectory on the vertical axis, representing the center of mass (CoM), as a quadratic function. We also use a polynomial to design the ZMP and CoM trajectory on the horizontal plane. To generate the CoM trajectory, the sliding mode controller is utilized to track the reference trajectory. To eliminate chattering of the sliding mode controller, the universal approximation property of the radial basis function (RBF) network is leveraged to approximate the switching law. Finally, simulation experiments are performed in CoppeliaSim, and the biped robot is capable of walking stably and quickly. © 2023 IEEE.

关键词： Stairs

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Multimodal surface material classification based on ensemble learning with optimized features 22

Multimodal surface material classification based on ensemble...

引用

22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Liu, Xiang Wu, Hancheng Fang, Senlin Yi, Zhengkun Wu, Xinyu CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781728162676

In this paper, we propose a novel method for multimodal material classification based on ensemble learning and optimized features. The proposed method consists of three key steps. Firstly, we extract a set of features for each modality. Compared to existing methods, the extracted features are relatively simple but more effective when they are incorporated into the classifiers. Then, the feature selection algorithms, including Multi-Cluster Feature Selection (MCFS) and Laplacian Score (LS) are employed to reduce the feature dimension due to the curse of dimensionality. Finally, an ensemble learning method is proposed to integrate the merits of different feature selection methods. The effectiveness of the proposed method is demonstrated on the LMT-108 surface material dataset which includes multiple modalities such as sound, acceleration, and image. The experimental results have shown that our approach performs better than the competing methods. © 2021 IEEE.

关键词： Learning systems

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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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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Design and simulation of a hip exoskeleton for lateral walking

Design and simulation of a hip exoskeleton for lateral walki...

引用

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

作者： Zhang, Zhewen Cao, Wujing Chen, Chunjie Yu, Hongliu Wu, Xinyu Meng, Qiaoling Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology

ISBN: (纸本)9781665436786

In this article, a rehabilitation exoskeleton assisted by lateral walking is designed to help patients with lower limb movement disorders to solve the problem of lateral walking assistance, so as to achieve the effect of rehabilitation training. Based on the characteristics of lateral movement of human body, the exoskeleton designed in this paper uses linear push rod motor and thigh binding to assist hip abduction and adduction respectively. Based on the research of human transverse movement gait, a reasonable linkage mechanism was designed and kinematics simulation was carried out. The dynamics model of human lower limbs was established and calculated with MATLAB simulation software. the moment of hip joint lateral movement is obtained. Finally, the structural parameters of the driving mechanism were analyzed by dynamic simulation, and the simulation results were compared with the target motion function to verify the rationality of the design. © 2021 IEEE.

关键词： MATLAB

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TactCapsNet: Tactile capsule network for object hardness recognition

TactCapsNet: Tactile capsule network for object hardness rec...

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

作者： Fang, Senlin Mi, Tingting Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Wu, Hancheng Yi, Zhengkun Wu, Xinyu Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781665436786

Hardness is one of the most essential tactile clues for robots to recognize objects. However, methods for robots to recognize hardness are limited. In this paper, based on the Capsule Network (CapsNet), we propose a novel tactile capsule network (TactCapsNet) for object hardness recognition. Specifically, we collect a tactile dataset on the silicone samples with three different shapes, and the silicone samples of each shape have thirteen hardness levels ranging from 0A (Shore A scale) to 60A at 5A intervals. Furthermore, we construct the tactile image as the input of the CapsNet to make full use of the spatio-temporal information of the tactile hardness dataset. The experimental results prove that the proposed approach achieves higher accuracy and quadratic weighted kappa (QWK) than support vector machine (SVM), long short-term memory (LSTM), convolutional neural network (CNN), and CapsNet. © 2021 IEEE.

关键词： Hardness

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A multi-task learning convolutional neural network for object pose estimation

A multi-task learning convolutional neural network for objec...

引用

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Wang, Yurui Jin, Shaokun Ou, Yongsheng Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Beijing China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

Estimating 6D poses of objects from RGB images is very crucial for robots to interact with the surrounding environment and to cooperate with humans. It is a challenging problem due to the various shapes of objects, the occlusions among objects, as well as the complexity of the scene. In this study, we present a new multi-task convolutional neural network for 6D object pose estimation, which also learns the object region in the image to further improve the estimation result. A weighted loss computed from the training samples is adopted to guarantee that the components of the estimated pose are equally accurate. Additionally, we sythesize new virtual data based on the existing training data so as to overcome the possible adverse situation caused by insufficiency of training data. Experiments on the YCB-video dataset are undertaken to validate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Semantic Segmentation

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Powered Super Tail: A Terrain-Adaptive Wheel-legged Robotic Limb to Assist Human’s Load Carriage

Powered Super Tail: A Terrain-Adaptive Wheel-legged Robotic ...

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

作者： Yanzhen Xiang Xiaoyu Yan Hanqi Su Nuo Chen Shangkun Guo Jielin Wu Yuquan Leng Chenglong Fu 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: (纸本)9781665431545

Load carriage is a common demand in humans’ daily life. Long time load carriage often causes significant energy expenditure for the body and may even bring physical problems like muscle strain. Variations in terrains and ground conditions are usually inevitable in such scenarios, which disrupts humans’ gait and leads to even more energy expenditure. In this paper, a wheel-legged robotic limb, Powered Super Tail (PST) to assist human’s load carriage in varying terrains and ground conditions is proposed. The PST system mainly consists of a support rod transferring load to the ground and a pair of stepping triple wheel groups adaptable to different terrain conditions including ground barriers, stairs and rough roads. Driven by a pair of motors, uplifting support for the load and appropriate force assistance could be effectively provided by the support rod, and thus energy expenditure of human body could be reduced. The system’s performance on human body was tested. It is shown that the stepping triple wheel group could successfully traverse barriers of different heights and ascend stairs. The effect of the system on human body standing and walking on flat ground were respectively evaluated under three experimental conditions including 1) with the load only (LOAD, 16.42 kg); 2) with the powered PST (PST_ON, 27.30 kg); 3) with unpowered PST (PST_OFF, 27.30 kg). And the percentage of metabolic power consumption reduction was the metric for evaluation. Experiments demonstrate the following results: during standing, the metabolic power consumption under PST_ON is lowered by 5.42% compared to LOAD and 12.56% compared to PST_OFF; and during walking, the metabolic power consumption is lowered by 20.85% compared to LOAD and 36.58% compared to PST_OFF.

关键词： Legged locomotion Measurement Power demand Mechatronics Roads Machine vision Wheels

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