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When Shannon Meets Nonstochastic Information: Communication and Estimation

IEEE Transactions on Automatic Control 2025年

作者： Zhang, Wentao Zuo, Zhiqiang Zhao, Rui Wang, Yijing Hu, Guoqiang Zhang, Hui Wang, Yaonan Hunan University School of Robotics Changsha410082 China Nanyang Technological University Continental-NTU Corporate Lab Singapore639798 Singapore Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore National Engineering Research Center for Robot Visual Perception and Control Changsha410082 China Hunan University College of Electrical and Information Engineering Changsha410082 China

This notes focuses on the issue of whether or not the state of a plant can be estimated when the inputs of communication channel involve noise and disturbance simultaneously. It turns out to calculate the maximum capacity of stationary memoryless multiple quasi-access channel under nonstochastic information theory. It is shown that the maximum information rate over such a channel is determined by a convex hull spanned by the zero-error capacities which are specified on the underlying sample spaces. Moreover, the state of a plant could be estimated provided that the maximal information rate is strictly greater than the topology entropy. © 1963-2012 IEEE.

关键词： Communication channels (information theory)

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Terrain environment classification and recognition for soft lower-limb exosuit

Terrain environment classification and recognition for soft ...

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

作者： Ni, Jiangpeng Wang, Zhuo Li, Feng Su, Zhilong Cao, Wujing Chen, Chunjie Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Advanced Technology Chinese Academy of Sci-ences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

ISBN: (纸本)9781665436786

Reliable environmental context prediction is critical for wearable robots (exoskeletons or prostheses) to assist terrain-adaptive locomotion. Inspired by the mechanism of human perception of the environment, the vision sensor can be introduced into the soft lower limb exosuit robot so that future environmental information can be used to improve the control. In this paper, a stable terrain classification and recognition system (TCRS) is designed for lower-limb soft exosuit robots. When the exosuit robot perceives the environment, it uses the residual network to train the terrain classifier to classify the terrain. During walking, the wearable soft exosuit robot collects the indoor environment, preprocesses, and marks the sampled images separately. The experimental results show that the classification accuracy is more than 90%, and the highest is 97.5%. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications. © 2021 IEEE.

关键词： Landforms

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Design of a human-robot interaction system for robot teleoperation based on digital twinning

Design of a human-robot interaction system for robot teleope...

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Telecommunications, Optics and Computer Science (TOCS), IEEE Conference on

作者： Ruishuo Xu Weijun Wang Wei Feng Zhaokun Zhou Boyoung An Ruizhen Gao Kaichen Zhou Hebei University of Engineerning Handan China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen 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 Shenzhen China

ISBN: (纸本)9781665470544

Human-robot interaction (HRI) research using vision for robot teleoperation is closely related to the development of the field of artificial intelligence. There are many teleoperation methods for imitating human postures by humanoid robots. However, it is not easy to fully parameterize human movements using collaborative robots because of the difference in morphology between humans and collaborative robots. This paper proposes a robot teleoperation and human-robot interaction method based on digital twin technology to try to solve this problem. Using this method, the operator can control the robot at a distance to accomplish complex tasks in an unstructured environment. The system integrates virtual reality technology to provide the operator with a first-person view of the robot. Then, a 3D human pose detection algorithm is proposed, through which the 3D coordinates of human torso, shoulder, elbow, and hand are captured and a digital twin model of the human body is created. In addition, a geometric vector method is proposed to calculate the upper limb joints of the human body and convert them into motion commands of the robot. Finally, teleoperated human-robot interaction experiments were conducted by the ELITE robot platform. We show that the system has good action imitation and dynamic behavior adaptation, which proves the effectiveness and feasibility of our proposed method.

关键词： Torso Three-dimensional displays Robot kinematics Biological system modeling Human-robot interaction Collaboration Virtual reality

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Joint Counting, Detection and Re-Identification for Multi-Object Tracking

arXiv

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arXiv 2022年

作者： Ren, Weihong Wu, Denglu Cao, Hui Chen, Xi'ai Han, Zhi Liu, Honghai The School of Mechanical Engineering and Automation State Key Lab of Robotics and Systems Harbin Institute of Technology Shenzhen518055 China The Shenzhen Duorou Intelligent System Co. Ltd. Shenzhen518055 China The School of Electrical Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Beijing110169 China

The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to missed or false detections. In this paper, we jointly model counting, detection and re-identification in an end-to-end framework, named CountingMOT, tailored for crowded scenes. By imposing mutual object-count constraints between detection and counting, the CountingMOT tries to find a balance between object detection and crowd density map estimation, which can help it to recover missed detections or reject false detections. Our approach is an attempt to bridge the gap of object detection, counting, and re-Identification. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to failure in crowded scenes, or depend on local correlations to build a graphical relationship for matching targets. The proposed MOT tracker can perform online and real-time tracking, and achieves the state-of-the-art results on public benchmarks MOT16 (MOTA of 79.7%), MOT17 (MOTA of 81.3%) and MOT20 (MOTA of 78.9%). Source code is available at https://***/weihong9/CountingMOT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Gesture Recognition Based on Attention and Dilated Causal Convolution

Gesture Recognition Based on Attention and Dilated Causal Co...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Zhou, Xin Liu, Jiaqing Huai, Yaping 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 Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 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 Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In recent years, based on surface electromyography (sEMG), great progress has been made in gesture recognition tasks, which is significant to the study of computer interaction and prosthesis control. Prior to this, many researches have focused on feature selection and time series modeling of data. However, it is difficult and complicated to manually perform feature extraction and feature selection on data. Besides, most of the time series modeling is based on recurrent neural networks with memory cell, that is, Long Short-term Memory (LSTM). Even though LSTM has memory gate, but cannot completely records historical information. In this paper, to address the above problems, we propose a Temporal Convolutional Network based on Attention mechanism (ATCN), in which Attention is used to characterize temporal features, and dilated causal convolution to capture more historical information. ATCN achieves 95.26% accuracy with this method on the public dataset NinaPro DB2, which exceeds the state-of-the-art accuracy of 5%. The experiments show that this approach is feasible and effective for gesture recognition tasks. Our contributions are as follows: Firstly, Attention mechanism is introduced into the field of prosthetic control. Then, ATCN is proposed for gesture recognition. Finally, ATCN achieves better results than LSTM on multiple public datasets. © 2022 IEEE.

关键词： Long short-term memory

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Individualization of Assistance Strategy for Hip Extension Assistance based on Emotion-in-the-Loop

Individualization of Assistance Strategy for Hip Extension A...

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IEEE International Conference on robotics and Biomimetics

作者： Zhuo Wang Fangliang Yang Lingxing Chen Tingwei He Chunjie Chen Xinyu Wu Shenzhen Institute of Adanced Technology Chinese Academy of Sciences Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Peng Cheng Laboratory Shenzhen China Shandong Zhongke Advanced Technology Co. LTD Jinan China

The human-in-the-loop optimization strategy has successfully improved the performance of the exoskeleton, but only considering the effect of physiological parameters, such as individual metabolic cost and joint torque differences, are considered. From a psychological perspective, emotion is also a significant factor affecting human gait character, especially during walking, where there are differences in the biological hip torque under different emotions. Inspired by this, we developed an individualization assistance strategy for hip extension based on Emotion-in-the-Loop (EIL) to improve human-exosuit interaction using emotional feedback. First, we classified four typical emotions based on the Support Vector Machine (SVM), the average accuracy of which reached 92.8%. After that, measuring the biological hip moments under different emotions through a motion capture experiment and the individualization of assistance strategy for hip extension assistance was designed based on the result. In the metabolism experiments, the soft exosuit with the EIL method can significantly further reduce metabolic consumption by about 4.5%, 6.7%, and 3.7% under the emotion of excitement, depression, and fear, respectively, in addition to original metabolic consumption reduction of 7.6%, which demonstrated that applying different assistance forces considering emotional changes can considerably reduce metabolic consumption. In the future, this method can be additionally used on different wearable robots to improve the performance of human-robot interaction.

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Dynamic Modeling and Analysis of a 6-DOFs Industrial Robot Considering Joint and Link Flexibility

Dynamic Modeling and Analysis of a 6-DOFs Industrial Robot C...

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Workshop on Predictive Control of Electrical Drives and Power Electronics (PRECEDE)

作者： Guodong Shen Chunjie Chen Haibin Wang Lijuan Li Qiang Wang Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781665425582

In the practical applications of industrial robots, the joint and link flexibility usually lead to unwanted vibrations. To suppress the vibrations and improve industrial robots’ precision, the accurate and efficient dynamic models are usually needed. In this paper, the dynamic modeling and analysis of a flexible-joint flexible-link industrial robot with six degrees of freedom(DOFs) are studied. Firstly, Spong’s model is employed to depict joint flexibility, and Assumed Mode Method(AMM) is used to establish the finite-dimensional dynamic model of flexible link. The first-order mode of the link is considered and the modal functions obtained using fixed-free boundary conditions of Euler beams are employed. Then, based on the Kane method, the partial velocity relationship between each link and joint is deduced, and dynamic equations of the whole system are derived. Finally, the vibration characteristics of the end-effector are analyzed using the dynamic model obtained by the proposed Kane-based method. The results indicate that joint flexibility and link flexibility have an important influence on the accuracy of the end-effector trajectory. Furthermore, the research in this study can offer some useful rules for path planning and control to suppress the vibration of the multi-DOFs industrial robot.

关键词： Vibrations Analytical models Service robots Predictive models Mathematical models End effectors Power electronics

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Terrain recognition and gait cycle prediction using IMU

Terrain recognition and gait cycle prediction using IMU

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

作者： Wang, Zhuo Zhang, Yu Ni, Jiangpeng Wu, Xinyu Liu, Yida Ye, Xin Chen, Chunjie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SZ DJI Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9781665436786

It is well known that terrain recognition and gait cycle prediction are important for powered exoskeleton. However, only a few works have focused on the concerns of complexity of the control system caused by using redundant sensors. In this paper, only two IMU sensors are applied to collect information of the angle and angular velocity of the hip joint in the situation of level-ground walking, ramp ascent, and ramp descent. Based on information acquired from these two IMU sensors, two methods are proposed to achieve terrain recognition. One method uses the angle of the hip joint when the two legs intersect as the threshold of terrain recognition. It can identify the terrain (level-ground walking, ramp ascent, ramp descent) during stable walking, but it cannot recognize the transitional terrain (from level-ground walking to ramp ascent, from ramp ascent to ramp descent, and so on) and its robustness is limited. The other method selects the angle and angular velocity of the hip joints as the eigenvector, and uses SVM for terrain recognition. The accuracy of terrain recognition is improved from 69.7% to 100% after introducing the Gaussian kernel function instead of Linear kernel function. For gait cycle prediction, Wiener one step prediction is applied in predicting the GC. Compared to actual GC, the error from predicted GC based on mean prediction is more than 8.0%, while the error from Wiener on step prediction is less than 4.35%. © 2021 IEEE.

关键词： Forecasting

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

引用

2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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Real-time Terrain Recognition based on Transformer and Gait Prediction based on CNN-LSTM-Attention for Exosuit

Real-time Terrain Recognition based on Transformer and Gait ...

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

作者： Fangliang Yang Chunjie Chen Zhuo Wang Yinsheng Xu Yao Liu Xinyu Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab 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 Guilin University of Technology Guilin China

For wearable soft exosuits, an imprecise control strategy can easily injure the wearer, while real-time terrain recognition and accurate gait phase estimation can effectively improve the control strategy of the exosuit and make the wearer comfortable and safe. In this paper, a real-time terrain recognition algorithm based on Transformer and a gait estimation algorithm based on CNN-LSTM-Attention are implemented. The Transformer-based recognition of different terrains improves the recognition accuracy to some extent, and the CNN-LSTM-Attention based feature extraction for temporal signals such as gait phase is also extremely noticeable. Experiments show that the Transformer-based algorithm achieves 99.64 % recognition accuracy in six different terrain environments. In the gait phase estimation experiment, CNN-LSTM-CBAM achieved the best performance with an evaluation index r2 of 0.9221. The aforementioned terrain recognition algorithms and gait phase estimation algorithms may have a positive impact on soft exosuit and dynamic prosthetics research.

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