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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Liu, Pengming Bao, Sheng Du, Liang Yuan, Jianjun Zhang, Hongkai Luo, RuiQing School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China Shanghai Robotics Institute Shanghai University Shanghai China

ISBN: (纸本)9798350325706

Place recognition is a crucial technique for mitigating accumulated errors in simultaneous localization and mapping (SLAM). However, this remains an immensely challenging task. Existing place recognition algorithms are susceptible to failure during translation or rotation changes, lacking both translation and rotation invariance simultaneously. In this paper, we adopt and enhance the global descriptor by incorporating principal component analysis (PCA) to align the current keyframe with the query keyframe to a consistent azimuth angle. Furthermore, to ensure algorithm speed, our method is primarily based on non-ground points, and we propose a novel iterative-based fast non-ground point extraction method. We extensively compare our proposed algorithm with other state-of-the-art algorithms (Scan Context, Intensity Scan Context, STD). All experimental results demonstrate that our proposed algorithm achieves higher precision and recall rates in loop closure detection. © 2023 IEEE.

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Kinematics analysis of lower limb rehabilitation robot based on D-H method 41

Kinematics analysis of lower limb rehabilitation robot based...

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第41届中国控制会议

作者： Kui Jie Wangyong He Renjie Gui School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

At present,rehabilitation robots usually work together with patients and train them to achieve therapeutic effects by assisting them to complete movement within a certain *** this paper,it analyzes the average length and proportion of Chinese human lower limbs,as well as the range of extreme motion and comfortable motion of each joint of human lower *** on the error transfer matrix of rigid body connecting rod,the three-DOF lower limb rehabilitation robot human lower limb static pose error model and human/machine integration model are *** D-H method was used to solve the kinematic function between the angle of each joint and the position of the end of human lower *** analysis of the kinematics of human lower limbs is to calculate its inverse kinematics *** the motion control of rehabilitation robots,the inverse kinematics is more important than the forward kinematics,which is the basis for rehabilitation robot to realize trajectory planning under the training mode of passive trajectory traction,as well as the basis for compliant ***,a simulation model is established with Matlab software,and the simulation results show the feasibility and safety of the rehabilitation training process.

关键词： Lower limb rehabilitation robot Range of motion Human kinematics

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Grasp Detection with Hierarchical Multi-Scale Feature Fusion and Inverted Shuffle Residual

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Tsinghua Science and Technology 2024年第1期29卷 244-256页

作者： Wenjie Geng Zhiqiang Cao Peiyu Guan Fengshui Jing Min Tan Junzhi Yu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Grasp detection plays a critical role for robot *** pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and ***,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are *** is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains ***,the channel information in high-level feature is also not well ***,the performance of grasp detection is *** solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle *** low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature *** a hierarchical fusion guarantees the quality of grasp ***,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective *** such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the ***,an information enhancement module is added before the encoder to reinforce input *** proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.

关键词： grasp detection hierarchical multi-scale feature fusion skip connections with attention inverted shuffle residual

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Deep Learning for EMG-based Human-Machine Interaction:A Review

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IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 512-533页

作者： Dezhen Xiong Daohui Zhang Xingang Zhao Yiwen Zhao State Key Laboratory of Robotics Shenyang Institute of AutomationInstitutes for Robotics and Intelligent ManufacturingChinese Academy of SciencesShenyang 110016 University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Robotics Shenyang Institute of AutomationInstitutes for Robotics and Intelligent ManufacturingChinese Academy of SciencesShenyang 110016China

Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)*** how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a ***,many EMG pattern recognition tasks have been addressed using deep learning *** this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based *** overview of typical network structures and processing schemes will be *** progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be *** issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be *** attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep *** hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI ***,possible future directions will be presented to pave the way for future research.

关键词： Accuracy deep learning electromyography(EMG) human-machine interaction(HMI) robustness

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Trajectory Control of Multi-Axis Robotic Arms Based on Physics-Informed Neural Networks and Nonlinear Model Predictive Control

Trajectory Control of Multi-Axis Robotic Arms Based on Physi...

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2024 China automation Congress, CAC 2024

作者： Wang, Yiqing Xia, Changqing Jin, Xi Xu, Chi Zhang, Yanzhu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350368604

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive Control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive Control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities. © 2024 IEEE.

关键词： Extended Kalman filters

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Synchronous Control Method and Simulation Research of Multi-degree of Freedom Humanoid Space Manipulator 2

Synchronous Control Method and Simulation Research of Multi-...

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2nd International Conference on Service robotics, ICoSR 2023

作者： Yang, Tao Xu, Fang Du, Baosen Ni, Yongjian Yu, Chao Shi, Yan Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Beijing China Beijing Institute of Precision Mechatronics and Controls Laboratory of Aerospace Servo Actuation and Transmission China Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China

ISBN: (纸本)9798350304688

The high-precision multi-joint synchronous control problem of the 7 DOF humanoid manipulator is studied. The control system uses a distributed control architecture based on EtherCAT. synchronization error causes tracking error between the end trajectory of the manipulator with theoretical trajectory. In order to achieve the purpose of high-precision synchronous control of manipulators, the kinematics model of manipulators is analyzed, and the method of network distributed clock control mode based on EtherCAT is proposed. Firstly, according to the source of synchronous error of the manipulator, the distributed network clock is used to calibrate the controller master station and the servo slave station to reduce the sampling and control synchronization error of each joint, and then improve the real-time performance of the main station Linux operating system. The station position command synchronization error is calibrated. Finally, the semi-physical simulation system of the joint and manipulator controller in the ring is designed. The simulation experiment analysis of the humanoid manipulator in the joint space and Cartesian space is carried out. The results show that the clock is distributed based on the EtherCAT network. The multi-joint synchronization control method can realize high-precision position control of the humanoid manipulator. © 2023 IEEE.

关键词： Manipulators

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Illumination Adaptive Identification Algorithm of a Reconfigurable Modular Robot

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Instrumentation 2024年第1期11卷 79-87页

作者： Fangyi Xing Cheng Xu Yanming Wu Hongwei Gao School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang 110158 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 School of Instrument Science and Engineering Southeast UniversityNanjing 210096 School of Automation Shenyang Aerospace UniversityShenyang 110135

Reconfigurable modular robots feature high mobility due to their unconstrained connection *** by the snake multi-joint crawling principle,a chain-type reconfigurable modular robot(CRMR)is designed,which could reassemble into various configurations through the compound joint ***,an illumination adaptive modular robot identification(IAMRI)algorithm is proposed for *** first,an adaptive threshold is applied to detect oriented FAST features in the robot ***,the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition *** matching features,an improved random sample consensus algorithm is employed to eliminate the mismatched ***,the reconfigurable robot module is identified effectively through the perspective *** with ORB,MA,Y-ORB,and S-ORB algorithms,the IAMRI algorithm has an improvement of over 11.6%in feature uniformity,and 13.7%in the comprehensive indicator,*** IAMRI algorithm limits the relative error within 2.5 pixels,efficiently completing the CRMR identification under complex environmental changes.

关键词： reconfigurable modular robot visual identification feature detection feature matching

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A Battery Control Strategy for Sequential Utilization of Energy Storage Systems in Power Batteries 7

A Battery Control Strategy for Sequential Utilization of Ene...

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7th IEEE Advanced Information Technology, Electronic and automation Control Conference, IAEAC 2024

作者： Li, Bowen Cui, Shijie Zhu, Lizhong Shenyang Ligong University College of Automation and Electrical Engineering Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Liaoning China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350339161

With the widespread adoption of energy storage systems utilizing power batteries, battery lifespan degradation has become a primary constraint on system performance. To address this issue, this study proposes a lifespan-optimized battery control strategy. Firstly, a battery lifespan calculation model for the energy storage system is established, taking into detailed consideration key parameters such as battery State of Charge (SOC). Secondly, a control strategy based on SOC for charging and discharging is designed, aiming to minimize battery lifespan consumption to the greatest extent possible. Finally, experimental results demonstrate that the proposed control strategy significantly improves the lifespan of the energy storage system compared to existing strategies. This research provides an effective means of control for the long-term stable operation of power battery systems. © 2024 IEEE.

关键词： Charging (batteries)

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Physics-Informed AUV Modeling Method and Application Under Current Disturbances 6

Physics-Informed AUV Modeling Method and Application Under C...

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6th International Conference on robotics and Computer Vision, ICRCV 2024

作者： Zhao, Yifeng Hu, Zhiqiang Geng, Lingbo Zhang, Shaoze Li, Xinmao Shenyang Institute of Automation State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331527426

In this paper, an improved method for modeling the dynamics of an Autonomous Underwater Vehicle (AUV) is proposed, incorporating the impact of ocean currents on AUV navigation. Initially, the concept of Physics-informed Neural Networks (PINN) is introduced, which integrate the AUV dynamics equations with deep neural networks to establish an AUV model capable of long-term forecasting under calm water conditions. By integrating the actual navigation data of the AUV with the established dynamic model in calm water, the current estimation is derived, which is then utilized to adjust the output values of the calm water model. Through the implementation of the Model Predictive Control (MPC) algorithm based on the modified AUV dynamics model, optimal control strategies for trajectory tracking under the influence of current disturbances are developed. The simulation results demonstrate that the proposed modeling approach can accurately forecast the AUV's navigation status in current conditions. Furthermore, the MPC controller, based on this approach, can proficiently and consistently accomplish AUV trajectory tracking tasks in ocean current environments. This research provides a novel perspective on AUV dynamic modeling and control, with potential implications for various related research fields. © 2024 IEEE.

关键词： Predictive control systems

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Analysis on steering characteristics of crawler pipeline robot

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High Technology Letters 2023年第1期29卷 60-67页

作者：耿林康 RAO Jinjun LEI Jingtao School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444P.R.China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai 200444P.R.China

In order to improve the elbow passing performance and different diameter adaptability of pipeline robot,a supported crawler pipeline robot is designed,which adopts screw nut mechanism and hinge four-bar mechanism to adapt to the complex environment such as variable diameter pipeline and *** steering characteristics passing through the elbow are studied,the kinematic of pipeline robot bending steering is established,the geometric constraint(GC)and steering constraint(SC)in the elbow are analyzed,and the steering experiment is *** results show that the robot can pass through the elbow by the SC *** SC model can reduce the motor current and energy consumption when the robot passes through the elbow.

关键词： crawler pipeline robot steering characteristics geometric constraint(GC) steering constraint(SC)

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