检索结果-内蒙古大学图书馆

NA-CPG: A robust and stable rhythm generator for robot motion control

Biomimetic Intelligence & robotics 2022年第4期2卷 30-38页

作者： Ru Tong Changlin Qiu Zhengxing Wu Jian Wang Min Tan Junzhi Yu State Key Laboratory of Management and Control for Complex 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

Central pattern generators(CPGs)have been widely applied in robot motion control for the spontaneous output of coherent periodic ***,the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces,and the coupling of oscillators affects the network output accuracy in complex topological ***,CPGs may diverge when parameters change drastically,and the divergence is irreversible,which is catastrophic for the control of robot ***,normalized asymmetric CPGs(NA-CPGs)that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are ***-CPGs can realize parameter decoupling,precise amplitude output,and stable and rapid convergence,as well as asymmetric output ***,it can effectively cope with large parameter changes to avoid network oscillations and *** optimize the parameters of the NA-CPG model,a reinforcement-learning-based online optimization method is further ***,a biomimetic robotic fish is illustrated to realize the whole optimization *** demonstrated that the designed NA-CPGs exhibit stable,secure,and accurate network outputs,and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7%and 41.7%,*** proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.

关键词： Normalized asymmetric CPGs Rhythm generator Motion control Robotic fish

来源：评论

学校读者我要写书评

暂无评论

Data-driven design of high bulk modulus high entropy alloys using machine learning

Journal of Alloys and Metallurgical Systems

引用

Journal of Alloys and Metallurgical Systems 2024年 8卷

作者： Jain, Sandeep Jain, Reliance Kumar, Vinod Samal, Sumanta Department of Metallurgical Engineering and Materials Science Indian Institute of Technology Indore Indore453552 India School of Advanced Materials Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Automation & Robotics Engineering Prestige Institute of Engineering Management and Research Madhya Pradesh Indore452010 India

In the current research, machine learning (ML) models were used as a tool for predicting the bulk modulus of High Entropy Alloys (HEAs). ML was employed to optimize HEA compositions for superior bulk modulus values. The study assessed five regression models: Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Support Vector Regression (SVR), and Lasso regression. The XGB regression model delivered the best results, with an R-squared (R2) value of 95.2 % and an RMSE of 2.6 % on the validation dataset. The XGB model's performance was further validated by experimental work, showing an R2 value of 94.8 % and an RMSE of 3.6 %. The R-squared, RMSE, and MAE values during training, testing, and validation for the XGB model ranged from 93.2 % to 99.62 %, 0.97 to 3.64, and 0.12 to 1, respectively. Furthermore, we used the top three trained models to predict the bulk modulus of six new HEAs that were not part of the training, testing, or validation datasets. These predictions achieved R² values of 94.8 %, 93.4 %, and 92.4 %, RMSE values of 3.6 %, 4.1 %, and 4.4 %, along with MAE values of 3.4 %, 3.8 %, and 4.1 %, for the XGB, Lasso, and SVR models, respectively. This work advances the field by bridging the gap in HEA discovery and property evaluation, offering novel methods for designing HEAs with desirable bulk modulus values, and unlocking new possibilities for HEA applications. © 2024 The Authors

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Geometric design of a kirigami gripper with form-enclosing grasping mode

Geometric design of a kirigami gripper with form-enclosing g...

引用

IEEE International Conference on robotics and Biomimetics

作者： Bingyan Hu Liang Du Mingyuan Wang Sheng Bao Jianjun Yuan Shugen Ma School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China Shanghai Robotics Institute Shanghai University Shanghai China Department of Robotics Ritsumeikan University Shiga Japan

Although various grasp strategies have been proposed in soft gripper area, form-enclosing grasping mode always plays a marginal role because of the challenges to calculate the deformations of soft materials. In this study, we propose a easy-to-make kirigami gripper and a geometric optimization method to obtain the fingers boundary shape that achieves a complete form-enclosing grasping after bending. We used a FEM model to represent the finger shape and calculated the finger boundary, and then used a iterative method to find the best finger shape that generates the form-enclosing grasping result. The optimization method is based on the minimum energy rule, and has the advantages of being computationally efficient and being universal for optimizing the boundary of gripper with form-enclosing grasping mode. The effectiveness of the proposed method was validated by ANSYS simulation and practical experimental results. Moreover, an objective functions and multiple geometric constraints were derived to quantitatively evaluate the gripper designs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modular design automation of the morphologies, controllers, and vision systems for intelligent robots: a survey

引用

Visual Intelligence 2023年第1期1卷 1-28页

作者： Li, Wenji Wang, Zhaojun Mai, Ruitao Ren, Pengxiang Zhang, Qinchang Zhou, Yutao Xu, Ning Zhuang, JiaFan Xin, Bin Gao, Liang Hao, Zhifeng Fan, Zhun International Cooperation Base of Evolutionary Intelligence and Robotics Guangdong Shantou China Department of Electronic Information Engineering Shantou University Shantou 515063 China School of Automation Beijing Institute of Technology Beijing 100081 China School of Mechanical Science & Engineering HUST Huazhong University of Science and Technology Wuhan 430074 China College of Science Shantou University Shantou 515063 China

Design automation is a core technology in industrial design software and an important branch of knowledge-worker automation. For example, electronic design automation (EDA) has played an important role in both academia and industry. Design automation for intelligent robots refers to the construction of unified modular graph models for the morphologies (body), controllers (brain), and vision systems (eye) of intelligent robots under digital twin architectures, which effectively supports the automation of the morphology, controller, and vision system design processes of intelligent robots by taking advantage of the powerful capabilities of genetic programming, evolutionary computation, deep learning, reinforcement learning, and causal reasoning in model representation, optimization, perception, decision making, and reasoning. Compared with traditional design methods, MOdular DEsigN automation (MODENA) methods can significantly improve the design efficiency and performance of robots, effectively avoiding the repetitive trial-and-error processes of traditional design methods, and promoting automatic discovery of innovative designs. Thus, it is of considerable research significance to study MODENA methods for intelligent robots. To this end, this paper provides a systematic and comprehensive overview of applying MODENA in intelligent robots, analyzes the current problems and challenges in the field, and provides an outlook for future research. First, the design automation for the robot morphologies and controllers is reviewed, individually, with automated design of control strategies for swarm robots also discussed, which has emerged as a prominent research focus recently. Next, the integrated design automation of both the morphologies and controllers for robotic systems is presented. Then, the design automation of the vision systems of intelligent robots is summarized when vision systems have become one of the most important modules for intelligent robotic systems. Then

关键词： Bond graph Evolutionary computation Intelligent robots Modular design automation Neural architecture search

来源：评论

学校读者我要写书评

暂无评论

A Hollow FBG-Based 3-Axis Force Sensor for Surgical Robots

A Hollow FBG-Based 3-Axis Force Sensor for Surgical Robots

引用

2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Li, Jie Deng, Weiquan Wang, Chongyang Cheng, Haoning Cui, Junlin Liang, Ming Liu, Hao Shenyang Jianzhu University School of Mechanical Engineering Shenyang110168 China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang1100169 China Key Laboratory of Minimally Invasive Surgical Robot Shenyang110016 China The General Hospital of Northern Theater Command of the Chinese People's Liberation Army Department of Cardiology China

ISBN: (纸本)9798350372601

The ability to perceive forces is crucial for surgical robots to perform operations safely and achieve effective treatment. This paper presents the design of a hollow force sensor based on fiber Bragg gratings (FBGs) to detect 3-Axis forces between the instrument and tissue. The sensor, consisting of an elastic body and four optical fibers with FBGs, is designed for convenient integration into instruments. Unlike most previous force sensors proposed by researchers, which fail to balance sensitivity across axes and have a hollow structure, the sensor designed in this paper positions the optical fibers away from the center to reserve sufficient space for the instrument channel. Additionally, the proposed elastic body comprises both an outer tube of flexible deformable beams and inner support pillars that enhance radial stiffness. This design ensures sensitivity along the axis while increasing stiffness against bending deformation radially. Furthermore, the force sensor is manufactured using 3D printing, making assembly simple and enabling easy interchangeability of instrument tips for various surgical instruments. Finite element simulations and experiments were conducted to verify the sensor's excellent linearity. The measurement range for each axis extends from 0 to 4 N. The axial resolution is 6 mN, while the radial resolution is 3.1 mN. At last, temperature compensation was implemented for the sensor environment to mitigate temperature-induced interference, ensuring precise measurements of three-Axis forces. The sensor is expected to be integrated into surgical robots to ensure surgical safety and enhance surgical quality. © 2024 IEEE.

关键词： Fiber Bragg gratings

来源：评论

学校读者我要写书评

暂无评论

Safety-Critical Scenario Generation by Causal Influence Detection 27

Safety-Critical Scenario Generation by Causal Influence Dete...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Yang, Yibing Zhang, Chi Xu, Linhai Ma, Shuangxun Li, Li Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Shaanxi Xi'an China School of Future Transportation Chang'an University Xi'an710064 China Tsinghua University BNRist Department of Automation Beijing100084 China

ISBN: (纸本)9798331505929

Appropriate behavior intervention for a specific background vehicle at the right moment is a crucial step in identifying safety-critical scenarios. Existing methods generate scenarios by estimating the data distribution, but when noise interventions are present, the emergence of safety-critical scenarios becomes colinearity with the noise interventions, potentially resulting in unnecessary interventions and unrealistic vehicle behaviors. To address this, we propose to integrate the causal relationship between the background vehicle behavior and the safety status of ego-vehicle as a prior into scenario generation. In this notion, we present a reinforcement learning framework with a causal influence detection module (CausalID). Specifically, we employ conditional mutual information to quantify the causal influence between vehicle sequential behaviors, and intervene on the background vehicle's behavior when the causal influence exceeds a certain threshold. Additionally, to prevent unrealistic collision-oriented driving, we sample the final intervention action from a set of candidate actions according to their probabilities of occurring in natural driving scenarios. Our experiments in 3-lane highway scenarios with multiple vehicles validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Vehicle safety

来源：评论

学校读者我要写书评

暂无评论

Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements

Interactive Learning of Physical Object Properties Through R...

引用

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

作者： Andrej Kruzliak Jiri Hartvich Shubhan P. Patni Lukas Rustler Jan Kristof Behrens Fares J. Abu-Dakka Krystian Mikolajczyk Ville Kyrki Matej Hoffmann Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Mechanical Engineering Department Faculty of Engineering New York University Abu Dhabi UAE Department of Electrical and Electronic Engineering Imperial College London London UK Department of Electrical Engineering and Automation Intelligent Robotics Group School of Electrical Engineering Aalto University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with measurement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object properties through Bayesian inference. Experimental evaluation demonstrates effective action selection compared to a baseline and correct termination of the experiments if there is nothing more to be learned. The algorithm proved to behave intelligently when presented with trick objects with material properties in conflict with their appearance. The robot pipeline integrates with a logging module and an online database of objects, containing over 24,000 measurements of 63 objects with different grippers. All code and data are publicly available, facilitating automatic digitization of objects and their physical properties through exploratory manipulations.

关键词： Uncertainty Databases Volume measurement Pipelines Measurement uncertainty Inference algorithms Probability distribution Bayes methods Intelligent robots Material properties

来源：评论

学校读者我要写书评

暂无评论

Human Hand Gesture Recognition System Using Body Sensor Network 4

Human Hand Gesture Recognition System Using Body Sensor Netw...

引用

4th International Conference on robotics and automation in Industry, ICRAI 2021

作者： Maheen, Asra Bin Shahzad, Moosa Asif, M. Usama Ahmad, Syed Fasih Zafar, Saqib Maqbool, H. Farhan Usman, Muhammad Arshad, Hira Department of Mechanical Mechatronics And Manufacturing Engineering Faisalabad Pakistan Human-Centered Robotics Lab National Center of Robotics And Automation Lahore Pakistan Pakistan Engineering Council Islamabad Pakistan Department of Mechanical Mechatronics And Manufacturing Engineering Faisalabad Pakistan

ISBN: (纸本)9781665423434

Wearable technology has gained massive popularity for applications in healthcare in recent years. Body sensor network (BSN) includes wearable sensors such as Myoware muscle sensor that can capture electromyography (EMG) signal from human muscles during a movement. This study involves the control of a 3d prosthetic hand using EMG data of basic hand gestures from healthy subjects and operating the prosthetic hand with this classified data. Artificial Neural Networks (ANN) are biologically inspired computational networks which help to build predictive models from large databases. Backpropagation combined with feedforward ANN is used to classify data here. Classification results showed an accuracy of about 91% for four-hand gestures - hand closed, hand open, pointing index finger, and pinch motion. The BSN-based prosthetic hand can facilitate rehabilitation of amputees, perform surgery using recorded EMG data from a surgeon's hands and reduce industrial manpower in the future. © 2021 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

GARField: Addressing the Visual Sim-to-Real Gap in Garment Manipulation with Mesh-Attached Radiance Fields

GARField: Addressing the Visual Sim-to-Real Gap in Garment M...

引用

IEEE International Conference on robotics and Biomimetics

作者： Donatien Delehelle Darwin Caldwell Fei Chen University of Genova Istituto italiano di Tecnologia Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

While humans intuitively manipulate garments and other textile items swiftly and accurately, it is a significant challenge for robots. A factor crucial to human performance is the ability to imagine, a priori, the intended result of the manipulation intents and hence develop predictions on the garment pose. That ability allows us to plan from highly obstructed states, adapt our plans as we collect more information and react swiftly to unforeseen circumstances. Conversely, robots struggle to establish such intuitions and form tight links between plans and observations. We can partly attribute this to the high cost of obtaining densely labelled data for textile manipulation, both in quality and quantity. The problem of data collection is a long-standing issue in data-based approaches to garment manipulation. As of today, generating high-quality and labelled garment manipulation data is mainly attempted through advanced data capture procedures that create simplified state estimations from real-world observations. However, this work proposes a novel approach to the problem by generating real-world observations from object states. To achieve this, we present GARField (Garment Attached Radiance Field), the first differentiable rendering architecture, to our knowledge, for data generation from simulated states stored as triangle meshes. Code is available on the project website.

关键词： Training Visualization Robot kinematics Clothing Pipelines Data collection Rendering (computer graphics) Neural radiance field State estimation Textiles

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Adaptive Multimodel Based Enhanced PID Controller With Kick Elimination for Smooth Power Control of Pressurized Water Reactor

Fuzzy Adaptive Multimodel Based Enhanced PID Controller With...

引用

IEEE International Conference on System, Computation, automation and Networking (ICSCAN)

作者： V. Govindan N. Pappa Department of Robotics and Automation Manakula Vinayagar Institute of Technology Puducherry India Department of Instrumentation Engineering MIT Campus Anna University Chennai India

This paper aims at design of Enhanced PID (PIDE) controller that eliminates kick based on adaptive fuzzy scheme for the higher order point -kinetic model of the pressurized water reactor (PWR) to assure stable smooth power operations under load following operating conditions, sudden external disturbances and model uncertainties. The dynamics of PWR type of nuclear reactor is significantly changing with power levels, hence fixed weighted multi-modelbased gain scheduled PID controller (FW -GSPID) and adaptive fuzzy based gain scheduled enhanced PID controller (AF-GSPIDE) are proposed with appropriately identified input variables to accommodate the scheduling of controller parameter as function of power level with ability to reject the kicks expected in the control signal. In order to regulate the reactor core power, the reactor model is linearized and the PID controller parameters are optimally tuned for different power levels with these multi-models. Most of the reported works on fuzzy based control schemes uses error and change in error as input variables for the controller design whereas the novelty in the proposed AF-GSPIDE is that the controller parameters K P , K I and K D are tuned based on process variable i.e. neutron density (n r ) and rate of change of process variable (dn r ) to ensure the smooth control under sudden changes in the load disturbances and parameter variations. With proper choice of membership functions and formulation of rule base along with newly identified inputs, the simulation results shows the improved potential of the proposed scheme at different power levels in the presence of uncertainties and external disturbances.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：