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

作者： Ma, Xinbo Jiang, Guolai Peng, Yuanzhe Ma, Tingchen Liu, Chao Ou, Yong-Sheng Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Key Laboratory of Human-Machine Intelligence-Synergy Systems University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems

ISBN: (纸本)9781665436786

In this paper, we consider various constraints to generate a recommended speed profile for the covered path of the mobile robot, and use the recommended speed profile as the control input to complete trajectory tracking. These speeds are affected by the constraints of the robot's kinematics and dynamics model and the constraints of the robot's environment. The speed planner is used in many scenarios of mobile robots, such as cleaning robots, disinfecting robots, etc., which can improve the safety and efficiency of tasks performed by robots. We introduce the commonly used traffic regulations in daily life into the generation of speed profile. In certain sections of highways or urban roads, there will be a road sign indicating the recommended speed of the section. And our paper is to generate the speed of each point for the given path as the recommended speed for the robot to drive for reference. We use a dynamic window method to update the current environment in real-time. And calculate the influence of the environment in the window on the current speed. Then we use a pure pursuit method to track the given path, and we define a safety cost function to express the safety of the trajectory. We demonstrated the efficiency and security of this method by using a simulation platform. © 2021 IEEE.

关键词： Speed

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Mastering Cooperative Driving Strategy in Complex Scenarios using Multi-Agent Reinforcement Learning

Mastering Cooperative Driving Strategy in Complex Scenarios ...

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

作者： Qingyi Liang Zhengmin Jiang Jianwen Yin Kun Xu Zhongming Pan Shaobo Dang Jia Liu Southern University of Science and Technology Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

With the advent of machine learning, several autonomous driving tasks have become easier to accomplish. Nonetheless, the proliferation of autonomous vehicles in urban traffic scenarios has precipitated other challenges, such as cooperative driving. Multi-Agent Reinforcement Learning (MARL) approaches have emerged as a promising solution, as they can successfully address the challenge by simulating the interactive relationship among autonomous vehicles (AVs). In this paper, we leverage Social Value Orientation to depict the behavioral tendencies of AVs, thereby enhancing the performance of MARL approaches. We also incorporate different scale features in the policy network to strengthen its representation ability. Moreover, effective reward functions are designed based on traffic efficiency, comfort, safety, and strategy. Finally, we validate our approach in an open-source autonomous driving simulator. Simulation results indicate that our proposed approach outperforms IPPO and MAPPO algorithms in terms of success rate, route completion rate, crash rate, and other metrics.

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Skeleton-Embedded Network for action recognition

Skeleton-Embedded Network for action recognition

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

作者： Li Luo Ziliang Ren Yong Qin Qieshi Zhang Xiangyang Gao Department of Computer Science Dongguan University of Technology Dongguan China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong China

Multimodality-based human action recognition is becoming an increasingly attractive topic as different modalities can provide complementary information. RGB and skeleton data have their pros and limitations for action recognition because they describe the action from distinctive views. Therefore, the fusion of these two modalities using their complementarity is meaningful to action recognition. However, it is difficult to completely utilize the supplementarity of RGB and skeleton data. In this paper, to take advantage of their complementary at the semantic level, A skeleton-embedded network(SE-Net) is proposed by us. The architecture is based on integrating their feature in the network not only a post-hoc fusion. With a sparse sampling strategy, which is efficient for learning using the whole action video. To examine the performance of our method, we conducted experiments on the wildly used datasets, obtaining an accuracy of 86.4% in C-sub and 88.2% in C-view on NTU-RGB+D 60. For NTU-RGB+D 120, the accuracy of 81.3% in C-sub and 83.4% in C-set is obtained.

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Lower limb exoskeleton design based on knee joint assistance

Lower limb exoskeleton design based on knee joint assistance

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

作者： Jiang, Peng Wang, Ye Shao, Tianqi Han, Changsoo Wang, Lin Li, Guanglin Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guangdong-Hong Kong-Macau Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China Hanyang University Department of Mechatronics Engineering Ansan15588 Korea Republic of Engineering School of Denis Diderot Paris Diderot University Paris75013 France

ISBN: (纸本)9781665436786

human mobility is vital to daily activities and can be directly linked to the living quality. This is an especially severe problem for the patients with walking dysfunction. There is a great need to develop viable mobility solutions to assist individuals and improve their living quality. Wearable gait-assisted exoskeletons are an effective solution to this growing problem. This paper presents a passive lower limb exoskeleton which can work on hip and knee joints for gait assistance. The hip exoskeleton is designed based on specially shaped torsion springs that work with cams for gravity compensation of the human thigh, converting the torque of the built-in torsion springs into a balanced torque of the hip and knee joints in the later stages of the gait stance phase to overcome the effects of gravity. The knee exoskeleton is designed with reference to a new spring-loaded gravity balanced structure used to carry single-degree-of-freedom rotational loads. When worn, it can help the lower leg to push out during gait swing state to compensate for falls due to the lack of lower leg swing. After the exoskeleton prototype was completed, 3D motion capture experiments were performed to obtain gait parameters. Finally, the effectiveness of the exoskeleton in assisting patients with walking dysfunction was verified using OpenSim simulation software. © 2021 IEEE.

关键词： Computer software

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ViT-based Terrain Recognition System for wearable soft exosuit

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Biomimetic intelligence & Robotics 2023年第1期3卷 57-64页

作者： Fangliang Yang Chunjie Chen Zhuo Wang Hui Chen Yao Liu Gang Li Xinyu Wua Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhen 518055China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Shenzhen 518055China SIASUN Robot and Automation CO. LtdShenyang 110169China

Terrain classification and force assistance strategies in complex environments have always piqued the interest of many *** wearable soft exosuits,inaccurate terrain recognition can easily introduce undesired assist forces that can easily injure the *** of these problems,we introduced a depth camera into the exosuit system,perform classification of terrain based on a Vision Transformer(ViT),and optimized the control algorithm,which is known as a ViT-Based Terrain Recognition System(TTRS).First,we used the Transformer algorithm to achieve a considerable classification effect in terrain *** also introduced terrain recognition as prior knowledge into the force assistance strategy of the exosuit,providing different force assistance to the exosuit in different ***,we performed human experiments with seven able-bodied people(six males and one female).The promising results demonstrate that our classification accuracy can reach 99.2%under six different terrains and that it can smoothly switch the force–assist curve in different terrains to better adapt to the complex terrain and improve the walking *** aforementioned terrain recognition algorithms and force–assist strategies may positively influence the study of soft exosuit,powered prostheses,and orthotics.

关键词： Vision Transformer(ViT) Depth camera Wearable soft exosuit Force assistance strategy Terrain recognition

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Latency Localization of Auditory Brainstem Response by Different Deep Learning Methods

Latency Localization of Auditory Brainstem Response by Diffe...

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

作者： Junyu Ji Xin Wang Desheng Jia Chunrui Zhao Yuchao He Ijaz Ahmad Mingxing Zhu Hongguang Pan Shixiong Chen Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems 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-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Otolaryngology Shenzhen Children's Hospital Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

Auditory brainstem response (ABR) is an evoked potential generated by the auditory brainstem nuclei in response to auditory stimuli. Hearing loss or impairment in the auditory pathway can be objectively reflected by extracting the latencies of the peaks of characteristic waves (especially wave V) in the clinic. The commonly used clinical method of peak localization is the manual labeling approach by subjective visualization. However, this method is time-consuming and highly dependent on the experience of the clinician. In this study, three different types of neural networks were proposed to realize the automatic intelligent recognition of the wave V position. Firstly, during the data preprocessing, the ABR data of humans was clipped and normalized. Then three types of neural networks, namely DNN, CNN, and RNN, were proposed for the latency localization of wave V. The performances of these three networks were further evaluated under different sound pressure levels and different error scales. The results showed that DNN was the best network for localizing the wave V position. The highest overall accuracy rate was 91.70% and the error scale was 0.1ms. This study showed that the proposed automatic method with neural networks can greatly reduce the workload of medical personnel in finding the ABR latencies and can assist doctors in further diagnosis.

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

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

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International Conference on Biomimetic intelligence and Robotics & Medical Robotics Forum, ICBIR+MRF 2024

作者： Chen, Hui Chen, Chunjie Wang, Zhuo Yang, Fangliang Pan, Heng Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 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. © 2024 The Authors.

关键词： RGB color model

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A Novel Gait Prediction Method for Soft Exosuit Base on Limit Cycle and Neural Network

A Novel Gait Prediction Method for Soft Exosuit Base on Limi...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Lingxing Chen Chunjie Chen Tao Fang Yu Zhang Youfu Liu Xinyu Wu CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems

ISBN: (数字)9781728162676

ISBN: (纸本)9781728162683

The important purpose of soft exosuit is to decrease the energy consumption of users by providing assistance. If there is something wrong with the judgment of the human gait during the assisting process, the assisting effect will be poor and even people's walking will be affected. A novel method is designed to predict human gait information in the paper, which utilizes the curve shape and mathematical characteristics of the Rayleigh oscillator equation in limit cycle to fit gait information. Only 1/4 gait cycle data is needed to input into the trained neural network to output an Rayleigh oscillator equation that can better predict the remaining gait cycle information. Data of four subjects on different terrains which include flat ground and upstairs are collected. Experiment results showed that the trained ANN model costs about 0.006s in CPU, it has a prediction speed similar to traditional prediction methods. Simultaneously the Rayleigh oscillator equation has good performance in predicting gait information, and it can show a higher stability and accuracy compared with traditional gait prediction methods.

关键词： Shape Neural networks Limit-cycles Predictive models Stability analysis Mathematical model Oscillators

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Stackelberg Game for Wireless Powered and Backscattering Enabled Sensor Networks

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China Communications 2024年第3期21卷 189-204页

作者： Lyu Bin Cao Yi Wang Shuai Guo Haiyan Hao Chengyao Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou 215006China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

关键词： backscatter communication energy interaction stackelberg game wireless powered sensor network

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Bionic design of a self-reconfigurable modular robot for search and rescue

Bionic design of a self-reconfigurable modular robot for sea...

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

作者： Gao, Guangju Liu, Jingshuai He, Yong Sun, Jianquan Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

This paper proposes a novel self-reconfigurable modular robot for search and rescue. The modular robot consists of two main structures, a planetary wheel driven robot body and a double unlocking system as the docking structure. By means of the planetary wheel structure, the robot has a strong flexibility to cross obstacles in a disaster environment. In virtue of the docking structure with double unlocking function, the self-reconfigurable ability has improved robot reliability. Further, the modular robot can be connected or disconnected according to the actual task requirements. Finally, to verify the stability and reliability, the finite element analysis is carried out. © 2021 IEEE.

关键词： Reliability analysis

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