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检索条件"机构=School of Robotics and Automation Engineering"

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Dynamics Modeling of Dual-Drive H-type Cartesian Robot with Replaceable Flexible Joints Based on the Pseudo-Rigid-Body Model 3

Dynamics Modeling of Dual-Drive H-type Cartesian Robot with ...

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3rd International Symposium on robotics and Intelligent Manufacturing Technology, ISRIMT 2021

作者： Ye, Liuying Chen, Silu Liu, Jicheng Wan, Hongyu Yu, Hongtao Zhang, Chi Yang, Guilin Shanghai University School of Mechatronic Engineering and Automation Shanghai China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9781665437189

The H-type Cartesian robot is widely used in highspeed and high-precision industrial automation and intelligent manufacturing. A dual-drive H-type Cartesian robot with a pair of replaceable flexible joints on two ends of its cross-beam is designed. Such design effectively reduces the concentrated stress at the junctions between moving carriages and the cross beam during high-speed repetitive motion. To correctly access the degree of freedom of the stage, the prismatic-revolute pseudo-rigid-body model is developed to equivalently replace the flexible joint. In this way, the semi-analytical dynamic model is derived by the Lagrange method with the correct setting of the generalized coordinates. The parameters in the model are identified based on the data-driven approach. Simulation results in both RecurDyn and MATLAB/Simulink cross-validate the accuracy of the model. © 2021 IEEE.

关键词： Degrees of freedom (mechanics)

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Industrial Wireless Network Access Selection Based on Multilevel Fuzzy Neural Network

Industrial Wireless Network Access Selection Based on Multil...

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Chinese Control and Decision Conference, CCDC

作者： Yan Xu Zhijun Gao Sichao Zhang School of Electrical and Control Engineering Shenyang Jianzhu 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 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In order to solve the issues in the heterogeneous industrial wireless network access selection such as delay and reliability, this paper present an industrial wireless network access selection based on multilevel fuzzy neural network. The algorithm divides the input attributes into three categories: network attributes, terminal attributes, and environment attributes. It synchronously inputs them into the fuzzy neural network to obtain the candidate network scores, which effectively reduces the number of fuzzy rules and reduces the algorithm complexity. Furthermore, this paper also consider the influence of the industrial environment on network selection and quantitatively reduce the number of switching and blocking rate. The simulation experimental results show that this algorithm is able to achieve fast network access selection in noisy and occluded industrial heterogeneous wireless networks, and the blocking rate can remain low in the presence of dense terminals.

关键词： Wireless networks Neural networks Switches Mean square error methods Interference Fuzzy neural networks Delays

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Aggregating Single-Wheeled Mobile Robots for Omnidirectional Movements

Aggregating Single-Wheeled Mobile Robots for Omnidirectional...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Meng Wang Yao Su Hang Li Jiarui Li Jixiang Liang Hangxin Liu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BI-GAI) Department of Advanced Manufacturing and Robotics College of Engineering Peking University Department of Robot Engineering School of Automation Science and Electrical Engineering Beihang University

This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation settings. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs.

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Robust-Tube Model Predictive Control for Piezoelectrically Actuated Fast Mechanical Switches for Hybrid Circuit Breakers

Robust-Tube Model Predictive Control for Piezoelectrically A...

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Artificial Intelligence and automation Control (AIAC), International Conference on

作者： Yu Chai Chen Yang Huijuan Dong Jie Zhao Yuri A. W. Shardt Bingfeng Ju ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Department of Automation Engineering Technical University of Ilmenau Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%.

关键词： Vibrations Uncertain systems Uncertainty Simulation Switches High-voltage techniques Predictive models Trajectory Approximation methods Predictive control

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Clamping Holder of Replaceable Tools for Medical Robot*

Clamping Holder of Replaceable Tools for Medical Robot*

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Yinjia Zhang Qian Yang Dengbo Zhang Xudong Ma Jie Tang Yuanzheng Hou Yida David Hu Junchen Wang Changsheng Li Shaolong Kuang Wenyong Liu Bai-Quan Su Medical Robotics Laboratory School of Intellgent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Department of Neurosurgery Xuanwu Hospital Capital Medical University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechanical Engineering and Automation Beihang University Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China Key Laboratory for Biomechaniscs and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

The replacement of surgical tools is one of the basic operations in performing surgery. However, this step is time-consuming in operational processes, and elevates the associated risks. Reason: The revision enhances clarity and employs more formal language suitable for an academic context. At present, medical robots still need manual operation to replace surgical tools. In order to enable the robot to change surgical tools, this study designed a clamping tool holder for changing surgical tools. The clamping tool holder is driven by a 6-degree of freedom robotic arm. In this paper, CAD model of the clamping tool holder is presented, the motion of the clamping tool holder analysis and pick up the tools of simulation operation. Finally, the prototype experiment verified that the clamping tool holder can hold and release surgical tools without manual assistance. The structure can realize the effect of saving manpower and improving operation efficiency.

关键词： Solid modeling Analytical models Medical robotics Prototypes Surgery Formal languages Manuals

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An Algorithm for Solving the Pose of Bio 3D Printing based on Neural Network

An Algorithm for Solving the Pose of Bio 3D Printing based o...

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第34届中国控制与决策会议

作者： Jian Liu Zili Song Huixuan Zhu Hang Yuan School of Information and Control Engineering Shenyang Jianzhu University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences

Obtaining the pose state information of the printing nozzle in real time and performing error compensation is an important means to improve the accuracy of biological 3D *** pose solving methods such as Newton iteration method and Analysis method have problems such as difficulty in selecting the initial value and slow calculation *** solve the above problems,a biological 3D printing pose solution algorithm based on the Elman neural network optimized by genetic algorithm is ***,the kinematics model of the biological 3D printing platform is established,then the GA-Elman neural network is established and the inverse solution of the platform is used as a training sample,and the pose information is solved by the method of network learning,and finally the accuracy of the algorithm is verified by *** simulation results show that the algorithm has high calculation accuracy and fast solution speed,and can quickly and accurately solve the pose of biological 3D printing.

关键词： Biological 3D printing Pose solving Neural Network Real time

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Single Segment Controllable Continuum Robot: A Pilot Study*

Single Segment Controllable Continuum Robot: A Pilot Study*

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Xu-Dong Ma Qian Yang Fengtong Wu Yinjia Zhang Yida David Hu Wenyong Liu Junchen Wang Changsheng Li Jie Tang Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intellgent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Neurosurgery Xuanwu Hospital Capital Medical University Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

In existing continuum robots, not every segment within a section is controlled, limiting the full utilization of their flexibility and making navigation through narrow and continuously curved spaces challenging. This paper introduces a single-section controllable continuum robot where each segment can be independently controlled via driving wires. Firstly, we designe the structure and connection method of the continuum robot segments. Secondly, we establish the kinematic model of the single-section controllable continuum robot, enabling independent control of each segment through the driving wires. Finally, we compares and tests the fitting accuracy of different configurations of continuum robots with controllable parts containing 1, 3, 5, and 7 segments in one section, regarding their ability to fit the same curve. The single-section controllable continuum robot can independently control the position of each joint within the continuum, demonstrating superior curve fitting capability and enabling navigation through narrow, continuously curved spaces.

关键词： Target tracking Limiting Navigation Wires Fitting Kinematics Aerospace electronics

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MODELING OF RESIDUAL STRESS DURING EDM OF AISI 4340 FOR MARINE PROPULSION APPLICATION

MODELING OF RESIDUAL STRESS DURING EDM OF AISI 4340 FOR MARI...

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作者： Husain Rizvi, Syed Asghar Sahu, Rohit Butola, Ravi Saraswat, Manish Sharma, Ajay Bhatia, Harpreet S. Khwaja Moinuddin Chishti Language University IIM Road Lucknow India GL Bajaj Institute of Technology and Management Greater Noida India University School of Automation & Robotics GGSIP University East Campus Delhi India LLOYD Institute of Engineering and Technology UP Greater Noida India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Centre of Research Impact and Outreach Chitkara University Punjab Rajpura140401 India

This investigation is conducted to estimate the extent of residual stress induced in the workpiece when machined on EDM. The Residual stresses induced post machining a product can led to lower life and inadequate failures during service. AISI 4340 finds its applicability in propulsion parts of marine engine and thus chosen as the material for study. Tungsten-Copper is selected as material for tool. Response Surface Methodology (RSM) with Central Composite Design (CCD) is utilized preparing the trails using current, on-time of pulse, voltage, and duty factor as machine variables. X-Ray Diffraction (XRD) was performed to estimate the d-space lattice of machined as well as un-machined specimens. Furthermore, Scanning Electron Microscopy (SEM) was executed to analyze effect of residual stress on surface post machining. The model suggested that voltage and on duration were crucial factors for residual stress while duty factor and current were less influential. Residual stress in the machined surface results from gradual heating and cooling during machining. The developed model was predicted to be accurate through the validation test. The micro-cracks resulted from the thermal stresses developed during machining of the workpiece. ©2024: The Royal Institution of Naval Architects.

关键词： Residual stresses

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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Instruction-Following Long-Horizon Manipulation by LLM-Empowered Symbolic Planner

Instruction-Following Long-Horizon Manipulation by LLM-Empow...

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IEEE International Conference on robotics and Biomimetics

作者： Zhihao Li Junjia Liu Zhuo Li Minghao Yu Tao Teng Shunbo Zhou Miao Li Tin Lun Lam Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong SAR Hong Kong Center for Logistics Robotics Hong Kong SAR Computing Technologies Co. Ltd. Shenzhen China Institute of Technological Sciences Wuhan University Wuhan China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

One of the grand goals in robotics is to develop a versatile robot capable of executing a wide array of tasks based on language instructions. These tasks often involve long-horizon manipulation due to the complexity of the real-world tasks and the high-dimensional action space of the dual-arm mobile robots. This work proposes a generalizable framework that combines a Large Language Model, a Visual-language Model, and symbolic planning to address long-horizon bimanual mobile manipulation tasks. We use the Large Language Model as the commonsense reasoner to break down the task into a set of subgoals and generate goal-oriented state queries. Subsequently, we fine-tune a Visual-language Model to obtain answers to queries. Finally, we propose a Planning Domain Definition Language translator that translates the subgoal and the goal-oriented states into a symbolic planning problem that can be solved by search-based planners. The experiments show that the proposed method has generalization ability in novel objects and instruction. It outperforms the baseline methods across all the configurations. Moreover, our approach exhibits competence in increasingly complex tasks, encompassing those that demand upward of 10 planning steps.

关键词： Translation Large language models Scalability Humanoid robots Detectors Search problems Generators Planning Complexity theory Mobile robots

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