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IEEE International Conference on Cyber technology in Automation, Control, and intelligent systems

作者： Chen Yang Liu Xin Wei Shimin Zhang Ying Huang Qineng School of Automation Beijing University of Posts and Telecommunications Beijing PR China Beijing Institute of Spacecraft System Engineering Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing PR China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

In this paper, the trajectory planning problem of a 3-DoF parallel wrist mechanism based on SimMechanics is proposed, which is a symmetric spherical 3-RRR parallel mechanism. The formulation of the inverse kinematics for the mechanism is firstly established by D-H matrix method, and the analytical solution is obtained. Then, the trajectory planning analysis of the mechanism is performed. Finally, the mechanism model is constructed based on the SimMechanics module of Matlab/Simulink, and the trajectory planning simulation of the mechanism is carried out. The simulation results agree well with the theoretical results, which indicate that the proposed method is feasible and convenient for the trajectory simulation of parallel mechanisms.

关键词： Kinematics Trajectory Conferences Automation intelligent systems Wrist

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Prediction of PM2.5 concentration based on PSO-LSSVR 8

Prediction of PM2.5 concentration based on PSO-LSSVR

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8th IEEE Data Driven Control and Learning systems Conference, DDCLS 2019

作者： Li, Jihan Li, Xiaoli Wang, Linkun Li, Yang Wang, Kang Beijing University of Technology Faculty of Information Technology 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Advanced Innovation Center for Future Internet Technology Engineering Research Center of Digital Community Ministry of Education Beijing100124 China Instrumentation Technology Economy Institute Beijing100055 China Communication University of China Beijing100024 China

ISBN: (纸本)9781728114545

To accurately predict the concentration of PM2.5 in the atmosphere, this paper establishes LSSVR prediction model based on historical data of atmospheric PM2.5 concentration. The parameters of LSSVR model are optimized by particle swarm optimization algorithm (PSO). According to PM2.5 concentration data per hour and meteorological conditions from June to August 2017 in Beijing, other PM2.5 concentration prediction models are established, which include ANN prediction model and $\varepsilon$-SVR prediction model. By comparing the prediction errors of these three prediction models, the calculated mean absolute error of the ANN prediction model was 25.24%, the mean absolute percent error of $\varepsilon$-SVR is 10.39%, and the mean absolute percent error of PSO-LSSVR model is 4.95%. The simulation results show that the PSO-LSSVR model is better than ANN model and $\varepsilon$-SVR model, and the PSO-LSSVR model has less computational time and reduces the complexity of the algorithm. Therefore, the proposed PSO-LSSVR algorithm is effective and reliable by predicting PM2.5 concentration. © 2019 IEEE.

关键词： Errors

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A lightweight soft exoskeleton in lower limb assistance

A lightweight soft exoskeleton in lower limb assistance

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Chinese Automation Congress (CAC)

作者： Yu Zhang Zhuo Wang Chunjie Chen Tao Fang Ruimei Sun Yanjie Li School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China BeiJing Aerospace Wanyuan Science Technology Co. Ltd. Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The soft exoskeleton is a wearable robot which is functioned to improve the capacity of human walking or loading. The metabolic of human is increase with the additional load, one integrated design method is used to reduce this effect in this paper. Meanwhile, in order to enhance the convenience and decrease the disturbance to wearer, the wireless module is used to replace the signal line. The nonlinear characteristics and uncertainty of flexible material make the control of soft exoskeleton system be a challenge, a switching controller is proposed to deal with this problem in this paper. It consists of Iterative learning control (ILC) based stable controller and admittance control based unstable controller, functioned with wearers walking stable and not. An experiment of force tracking is conducted to verify the controller performance in this paper, and the result demonstrate that the tracking is well after 20 iteration. In order to evaluate the performance of assistance of soft exoskeleton, five health adult male take part in metabolic experiment By compare the metabolic rate between the wear soft exoskeleton and not wear, the metabolic rate are decrease 7.33%, 14.56% and 10.45% with the walking speed of 3km/h, 5km/h and 7km/h, which are corresponding absolute values decrease of 0.46W/kg, 1.60W/kg and 1.21W/kg respectively.

关键词： AC motors Mechanical cables Wireless communication Pulleys Microprocessors Micromechanical devices Germanium

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2-Entity RANSAC for robust visual localization in changing environment

2-Entity RANSAC for robust visual localization in changing e...

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2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Yanmei Jiao Yue Wang Bo Fu Xiaqing Ding Qimeng Tan Lei Chen Rong Xiong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou P.R. China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing P.R. China

Visual localization has attracted considerable attention due to its low-cost and stable sensor, which is desired in many applications, such as autonomous driving, inspection robots and unmanned aerial vehicles. However, current visual localization methods still struggle with environmental changes across weathers and seasons, as there is significant appearance variation between the map and the query image. The crucial challenge in this situation is that the percentage of outliers, i.e. incorrect feature matches, is high. In this paper, we derive minimal closed form solutions for 3D-2D localization with the aid of inertial measurements, using only 2 point matches or 1 point match and 1 line match. These solutions are further utilized in the proposed 2-entity RANSAC, which is more robust to outliers as both line and point features can be used simultaneously and the number of matches required for pose calculation is reduced. Furthermore, we introduce three feature sampling strategies with different advantages, enabling an automatic selection mechanism. With the mechanism, our 2-entity RANSAC can be adaptive to the environments with different distribution of feature types in different segments. Finally, we evaluate the method on both synthetic and real-world datasets, validating its performance and effectiveness in inter-session scenarios.

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Learning a Faster Locomotion Gait for a Quadruped Robot with Model-Free Deep Reinforcement Learning

Learning a Faster Locomotion Gait for a Quadruped Robot with...

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IEEE International Conference on robotics and Biomimetics

作者： Biao Hu Shibo Shao Zhengcai Cao Qing Xiao Qunzhi Li Chao Ma College of Information Science and Technology Beijing University of Chemical Technology Beijing China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China

Quadruped robots have great agility, flexibility and stability, which enables them to walk through uneven terrain. Motion control of legged robots is always a difficult problem. Previous approaches mostly either use a predefined gait that results in clumsy and unnatural behavior, or use reinforcement learning approach to generate a gait strategy that needs longtime computation and elegant network design. In this paper, we present an effective approach that uses deep reinforcement learning with prior knowledge to optimize the gait of quadruped robot. By using a specific quadruped robot walking gait as a priori knowledge, this approach adopts the technique of distributed proximal policy optimization to optimize the search for better gait. The proposed approach does not require modelling of complex robots, and has good network convergence speed and learning effect. Simulation results demonstrate that our proposed approach converges faster than other deep reinforcement learning methods without prior knowledge. Besides, our achieved gait has higher speed that is 50% faster than the trot gait without optimization.

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3D printed near-infrared high-numerical aperture achromatic metalens

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iScience 2025年第6期28卷

作者： Wei, Heming Hu, Wenchen Hu, Jingtian He, Guoqiang Pang, Fufei Krishnaswamy, Sridhar Nedoma, Jan Marques, Carlos Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai 200444 China Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen China Center for Smart Structures and Materials Department of Mechanical Engineering Northwestern University Evanston 60208 IL United States Department of Telecommunications VSB – Technical University of Ostrava Ostrava 70800 Czech Republic CICECO – Aveiro Institute of Materials Physics Department University of Aveiro Aveiro 3810-193 Portugal Department of Physics VSB—Technical University of Ostrava Ostrava 70800 Czech Republic

Traditional optical Fresnel microlenses have limitations such as large size, limited optical quality for imaging, and low focusing efficiency in achromatic lenses with high NA. In contrast, metalenses rely on their subwavelength structure to modulate the phase distribution, resulting in smaller volumes and superior focusing performance. In this work, we inverse designed and fabricated an achromatic metalens with high-NA and broad wavelength range through direct laser writing using the two-photon polymerization technique. With a focal length of 19 μm, a thickness of 3.6 μm, and a numerical aperture of 0.8, the metalens exhibits an average focusing efficiency of 53.6% and an average half maximum width of 1.27 μm at the working wavelength. The measured average focusing efficiency is 50.4% within the bandwidth range of 1510 nm–1610 nm. The presented work demonstrates the great potential of 3D printing and inverse design for realizing functional meta-devices for aerospace sector. © 2025 The Author(s)

关键词： Metamaterials Optical materials Optics Photonics

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High Conductance Margin for Efficient Neuromorphic Computing Enabled by Stacking Nonvolatile van der Waals Transistors

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Physical Review Applied 2021年第4期16卷 044049-044049页

作者： Liping Xu (徐丽萍) Hao Xiong (熊浩) Zhichao Fu (付志超) Menghan Deng (邓梦晗) Shuiyuan Wang (王水源) Jinzhong Zhang (张金中) Liyan Shang (商丽燕) Kai Jiang (姜凯) Yawei Li (李亚巍) Liangqing Zhu (朱亮清) Liang He Zhigao Hu (胡志高) Junhao Chu (褚君浩) Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai) Engineering Research Center of Nanophotonics & Advanced Instrument (Ministry of Education) Department of Materials School of Physics and Electronic Science East China Normal University Shanghai 200241 China Center for Advanced Electronic Materials and Devices School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Computer Science and Technology East China Normal University Shanghai 200062 China ASIC & System State Key Laboratory School of Microelectronics Fudan University Shanghai 200433 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan Shanxi 030006 China Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai 200433 China

High-performance artificial synaptic devices are key building blocks for developing efficient neuromorphic computing systems. However, the nonlinear and asymmetric weight update of existing devices has restricted their practical applications. Herein, floating gate nonvolatile memory (FG NVM) devices based on two-dimensional (2D) HfS2/h-BN/FG-graphene heterostructures have been designed and investigated as multifunctional NVM and artificial optoelectronic synapses. Benefiting from the FG architecture, the HfS2-based NVM device exhibits competitive performances, such as a high on:off ratio (>105), large memory window (approximately 100 V), excellent charge retention ability (>104s), and robust durability (>103 cycles). Notably, the artificial optoelectronic synapses based on HfS2 FG NVM show an impressive large conductance margin and good linearity, owing to the ultrahigh photoresponsivity and photogain of HfS2. The energy consumption of per spike in our artificial synapse is as low as 0.2 pJ. Therefore, a high recognition accuracy up to 91.5% of the artificial neural network on the basis of our HfS2-based optoelectronic synapse at the system level has been achieved, which is superior to other reported 2D artificial optoelectronic synapses. This work paves the way forward for all 2D material-based memory for developing efficient optogenetics-inspired neuromorphic computing in brain-inspired intelligent systems.

关键词： Electrical conductivity Optoelectronics Synapses 2-dimensional systems Artificial neural networks Transistors Transition metal dichalcogenides Lithography

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Method for constructing multi-dimensional feature map of malicious code

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Haocong Ma Ji Zhang Junhua Zhou Xiang Zhai Junjie Xue Hang Ji State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing 100854 China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing simulation Center Beijing 100854 China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

Malicious code is characterized by a large number of types, rapid increase in number, continuous update of transmission routes, and continuous enhancement of back analysis and back detection methods. Therefore, how to effectively detect and analyze malicious code has been a problem of great concern. This paper studies the features of binary file and disassembly file of malicious code, introduces the concept of information gain, and proposes a method to construct the multi-dimensional characteristic graph of malicious code. Finally, the convolutional neural network is used to classify the multi-dimensional feature graph of malicious code, which provides a new idea for the feature extraction of malicious code.

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Design and Analysis of a Flying-crawling Spherical Robot for Multi-mode Movement

Design and Analysis of a Flying-crawling Spherical Robot for...

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IEEE International Conference on robotics and Biomimetics

作者： Pengfei Lu Kun Xu Xilun Ding Shuiqing Jiang Zixin Tang Yaobing Wang Robotics Institute of School of Mechanical Engineering and Automation Beihang University Beijing China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China

A novel spherical robot named flying-crawling spherical robot is designed in this paper. It can fly or roll on ground with its single rotor and eight tail fins. When its limbs stretch out it can crawl on the supporting surface. Besides, with its limbs the robot can realize carrying objects, landing on uneven ground and walking on the supporting surface. Dynamic model of the robot in flight mode is established and the motion characteristics are analyzed. A PID control system is designed to enable the robot to achieve stable flight. Trot gait planning of the robot is given and a PID controller is designed for tail fins assistant in crawling mode. Corresponding simulations are carried out and the simulation results verify the proposed methods.

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