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2023 9th International Forum on Manufacturing technology and Engineering Materials, ICMTEM 2023

作者： Zhang, Ruming Guan, Kunpeng Wu, Chenchen Shenyang University of Technology Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Science and Technology Liaoning Anshan114051 China

Thermoset composites constructed with monolithic moulding can significantly reduce the number of parts and connectors, improve the reliability of the composite structure, and reduce production costs. For such composite components, the degree-of-cure (DoC) curve is normally considered to evaluate the curing process, so that the curing process-induced deformation and residual stress can meet the manufacturing requirements. In this paper, a method is proposed to predict the DoC curve based on the neural network. First, the DoC curves for AS4/3501-6 composite structures were obtained from ABAQUS and HETVAL subroutine, and the results were verified by published values. Next, the curves of temperature over time and DoC over time were generated to train the neural network. Finally, the back propagation (BP) neural network is optimized using the genetic algorithm (GA), and the DoC curve prediction model is created. The results demonstrate that, with a maximum error of 3.24%, the simulated curves essentially correspond with published values. Evaluation and validation analyses show that the prediction of the DoC curve using the GA-BP neural network yields high computational efficiency and accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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Improving Robot Social Perception in Human-Robot-Interaction Using Multi-modal Cues

Improving Robot Social Perception in Human-Robot-Interaction...

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Iberian robotics Conference (ROBOT)

作者： Josep Bravo Jonathan Cacace Devis Dal Moro Daniel Serrano Magí Dalmau-Moreno Robotics and Automation Unit Eurecat Technology Centre Catalonia Spain

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

This paper proposes a multi-modal framework for enhancing robot social perception in human-robot interaction applications. By integrating multiple sensory modalities-such as visual, auditory, and spatial data-robots can improve their understanding and responsiveness to human social cues. Our approach leverages advanced algorithms for object detection, face recognition, gaze tracking, and voice recognition, combined into a coherent system to improve interaction outcomes. A key result of this pipeline is the identification of the individual most engaged with the robot, enabling targeted interactions such as personalized dialogues and tailored assistive tasks. We demonstrate this framework's implementation in a socially assistive robot, showcasing notable enhancements in identifying and interacting with the most engaged user, thereby markedly improving both user engagement and the quality of interactions.

关键词： Visualization Face recognition Pipelines Human-robot interaction Speech recognition Object detection Gaze tracking Robot sensing systems Assistive robots Spatial databases

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Simulation and Experiment of Cardiopulmonary Resuscitation Robot 2

Simulation and Experiment of Cardiopulmonary Resuscitation R...

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2022 2nd International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2022

作者： Zou, Xianghua Li, Bin Wang, Cong Chang, Jian Li, Yanbin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Shenyang University of Technology Shenyang China

Aiming at the emergence of patients with outdoor cardiac arrest, to strive for more golden rescue time, a robot suitable for the rescue of patients with outdoor cardiac arrest is proposed. Combined with the medical research on the human thorax, the dynamic model of the human thorax is established, the kinematics and dynamics of the end effector mechanism are analyzed, and the robot is simulated for cardiopulmonary resuscitation in the human thorax with Adams software. The relationship between the stroke, speed, acceleration, driving torque, and time of the end effector is obtained. Second, the dynamic formula theory is simulated with Matlab software. The difference between the theoretical driving torque and the simulation driving torque is 5.2% and 2.3% respectively. Finally, the feasibility of the CPR operation of the robot is preliminarily verified through experiments. © Published under licence by IOP Publishing Ltd.

关键词： Resuscitation

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A Comprehensive Study on Weather Predicting automation Bot Using automation Software and Machine Learning

A Comprehensive Study on Weather Predicting Automation Bot U...

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Innovation in technology (APCIT), Asia Pacific Conference on

作者： Vedant Joshi Priya Jadhav Robotics and Automation Department Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Weather forecasting is crucial in our daily lives, influencing everything from when to walk outside to what to dress. In this era of automation and data-driven decision-making, a new and reliable way of weather prediction is now possible thanks to the collaboration of automation software and machine learning. This study closely investigates the development and implementation of an automation bot for weather forecasting that employs machine learning and automation software techniques. This study investigates the complexity of this automation system, the underlying algorithms, its ease of use, and the accuracy of the forecasts it generates. The study shows how this unique concept not only increases weather forecast accuracy but also mentions the procedure, making it more accessible to users in daily life. This study shows the significant advancement in weather forecasting by combining the use of automation software with data-driven insights from machine learning. This will help in a more accurate and precise real-time understanding of climate change.

关键词： Technological innovation automation Accuracy Machine learning algorithms Software algorithms Weather forecasting Machine learning

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Mechanical Properties Inside Origami-Inspired Structures: An Overview

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Applied Mechanics Reviews 2025年第1期77卷 011001页

作者： Yan, Peng Huang, Hailin Meloni, Marco Li, Bing Cai, Jianguo Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518052 China Harbin Institute of Technology Shenzhen518052 China Key Laboratory of C & PC Structures of Ministry of Education National Prestress Engineering Research Center Southeast University Nanjing211189 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518052 China Key University Laboratory of Mechanism Machine Theory and Intelligent Unmanned Systems of Guangdong Habin Institute of Technology Shenzhen518052 China

In recent decades, origami has transitioned from a traditional art form into a systematic field of scientific inquiry, characterized by attributes such as high foldability, lightweight frameworks, diverse deformation modes, and limited degrees-of-freedom. Despite the abundant literature on smart materials, actuation methods, design principles, and manufacturing techniques, comprehensive reviews focusing on the mechanical properties of origami-inspired structures are relatively rare and unsystematic. This review aims to fill this void by analyzing and summarizing the significant studies conducted on the mechanical properties of origami-inspired structures from 2013 to 2023. We begin with an overview that includes essential definitions of origami, classical origami patterns, and their associated tessellated or stacked structures. Following this, we delve into the principal dynamic modeling method for origami and conduct an in-depth analysis of the key mechanical properties of origami-inspired structures. These properties include tunable stiffness, bistability and multistability, metamechanical properties demonstrated by origami-based metamaterials, and bio-inspired mechanical properties. Finally, we conclude with a comprehensive summary that discusses the current challenges and future directions in the field of origami-inspired structures. Our review provides a thorough synthesis of both the mechanical properties and practical applications of origami-inspired structures, aiming to serve as a reference and stimulate further research. © 2025 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Degrees of freedom (mechanics)

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General closed-form inverse kinematics for arbitrary three-joint subproblems based on the product of exponential model

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Frontiers of Mechanical Engineering 2022年第2期17卷 85-101页

作者： Tao SONG Bo PAN Guojun NIU Jiawen YAN Yili FU State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China School of Mechanical Engineering and Automation Zhejiang Sci-Tech UniversityHangzhou 310018China

The inverse kinematics problems of robots are usually decomposed into several Paden–Kahan subproblems based on the product of exponential model. However, the simple combination of subproblems cannot solve all the inverse kinematics problems, and there is no common approach to solve arbitrary three-joint subproblems in an arbitrary postural relationship. The novel algebraic geometric (NAG) methods that obtain the general closed-form inverse kinematics for all types of three-joint subproblems are presented in this paper. The geometric and algebraic constraints are used as the conditions precedent to solve the inverse kinematics of three-joint subproblems. The NAG methods can be applied in the inverse kinematics of three-joint subproblems in an arbitrary postural relationship. The inverse kinematics simulations of all three-joint subproblems are implemented, and simulation results indicating that the inverse solutions are consistent with the given joint angles validate the general closed-form inverse kinematics. Huaque III minimally invasive surgical robot is used as the experimental platform for the simulation, and a master–slave tracking experiment is conducted to verify the NAG methods. The simulation result shows the inverse solutions and six sets given joint angles are consistent. Additionally, the mean and maximum of the master–slave tracking experiment for the closed-form solution are 0.1486 and 0.4777 mm, respectively, while the mean and maximum of the master–slave tracking experiment for the compensation method are 0.3188 and 0.6394 mm, respectively. The experiments results demonstrate that the closed-form solution is superior to the compensation method. The results verify the proposed general closed-form inverse kinematics based on the NAG methods.

关键词： inverse kinematics Paden-Kahan subproblems three-joint subproblems product of exponential closed-form solution

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Remaining Useful Life Prediction of Lithium-ion Batteries Based on Multi-graph-network model 43

Remaining Useful Life Prediction of Lithium-ion Batteries Ba...

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43rd Chinese Control Conference, CCC 2024

作者： Tao, Liujun Wu, Huaiyu Zheng, Xiujuan Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan430081 China

ISBN: (纸本)9789887581581

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for their safety and reliability, and it's an important issue of concern for battery management systems. The existing RUL prediction is mainly concentrated on a single source characteristics or a single model, and generally only the time features of the battery Health Indicators (HIs) are considered. This article proposes a multi-feature and multi-graph neural network fusion method for predicting the RUL of batteries. First of all, extract HIs from multiple aspects such as voltage, current, temperature curve and other aspects during partial charging and discharging processes, and construct HIs graph. Secondly, preliminary predictions of RUL were made using Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sample and Aggregate Network (GraphSAGE), respectively. In order to improve the prediction accuracy of the model, the model extracts spatial features from non-homologous sources and constructs an HI graph as the primary input. Inspired by the advantages of multi-model fusion, random forest regression is used for multi model fusion. To verify the effectiveness of the model, comparative experiments were conducted on the Massachusetts Institute of technology (MIT) battery dataset. The results indicate that the proposed method has better accuracy and stronger robustness in RUL prediction. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Lithium-ion batteries Multi graph neural network Multi source spatial features Prediction of remaining useful life Random forest regression

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Micro-Seismic Event Detection using statistical feature extraction and machine learning techniques 7

Micro-Seismic Event Detection using statistical feature extr...

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7th IEEE International conference for Convergence in technology, I2CT 2022

作者： Chakraborty, Mainak Das, Manisha Aruchamy, Srinivasan Indian Institute of Engineering Science and Technology School of Mechatronics and Robotics Shibpur India CSIR- Cmeri Robotics and Automation Group Durgapur India

ISBN: (纸本)9781665421683

In this paper, we propose a novel methodology for micro-seismic event detection using multiple statistical feature extraction and machine learning techniques. The method is based on amplitude thresholding and is grounded on the idea, that each relevant seismic event crosses a particular threshold value. We have created a unique dataset of movement, hammering and no-movement using off-the-shelve geophones and 16-bit analog to digital converter. The data is sampled at 10000Hz. Each recorded dataset is of duration 2 min. The data recorded is passed through amplitude thresholding method. Multiple statistical features like root mean square, standard deviation, variance, minimum value and maximum value are extracted from the dataset in both time and frequency domain. k-fold cross validation is done to obtain validation accuracy. We have explored various machine learning algorithms and the results are discussed here. Among all classifiers, Random Forest algorithm achieves the overall highest accuracy of 92.76%. © 2022 IEEE.

关键词： Decision trees

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Annotation-efficient learning of surgical instrument activity in neurosurgery

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Current Directions in Biomedical Engineering 2022年第1期8卷 30-33页

作者： Philipp, Markus Alperovich, Anna Lisogorov, Alexander Gutt-Will, Marielena Mathis, Andrea Saur, Stefan Raabe, Andreas Mathis-Ullrich, Franziska Health Robotics and Automation (IAR-HERA) Kit Karlsruhe Germany Carl Zeiss Ag Oberkochen Germany Carl Zeiss Meditec Ag Oberkochen Germany University Hospital Bern Bern Switzerland Health Robotics and Automation (IAR-HERA) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

Machine learning-based solutions rely heavily on the quality and quantity of the training data. In the medical domain, the main challenge is to acquire rich and diverse annotated datasets for training. We propose to decrease the annotation efforts and further diversify the dataset by introducing an annotation-efficient learning workflow. Instead of costly pixel-level annotation, we require only image-level labels as the remainder is covered by simulation. Thus, we obtain a large-scale dataset with realistic images and accurate ground truth annotations. We use this dataset for the instrument localization activity task together with a studentteacher approach. We demonstrate the benefits of our workflow compared to state-of-the-art methods in instrument localization that are trained only on clinical datasets, which are fully annotated by human experts. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Neurosurgery

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Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

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Engineering 2024年第2期33卷 90-107页

作者： Xinwei Wang Zirui Li Javier Alonso-Mora Meng Wang School of Engineering and Materials Science Queen Mary University of LondonLondon E14NSUK Department of Cognitive Robotics Delft University of TechnologyDelft 2628 CDNetherlands School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Chair of Traffic Process Automation “Friedrich List”Faculty of Transport and Traffic SciencesTU DresdenDresden 01069Germany

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent ***-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle ***,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world *** this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway *** this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time ***,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical *** construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction *** experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving *** efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway *** proposed risk assessment framework is promising for real-world applications.

关键词： Probabilistic collision detection Confidence awareness Probabilistic acceleration prediction Reachability analysis Risk assessment

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