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2024 International Conference on automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Zhu, Jia-Yang Sun, Yan-Ning Song, Yi-Tian Liu, Li-Lan Gao, Zeng-Gui Qin, Wei School of Mechatronic Engineering and Automation Shanghai University Shanghai China Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China School of Mechatronic Engineenng and Automation Shanghai University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350378658

The assembly process of complex products involves many process links, which is a dynamic process with multiple variables and complex coupling. Effective quality control of the assembly process plays an important role in improving the consistency of complex product quality. However, in the actual assembly process, even if the quality of each component meets the design requirements, deviations still gradually accumulate and spread in the process, and it is ultimately difficult to ensure the assembly quality. To effectively identify the key assembly features and the deviation propagation paths in the assembly process, this paper investigates the assembly deviation propagation problem of complex assemblies based on complex network theory. Firstly, based on the measured data, feature surface information, and assembly process, a coupled network model of parts-features is constructed for describing the complex assembly systems by taking assembly parts as nodes and assembly relationships as edges. Then, the node importance indexes of the assembly deviation propagation network are measured to identify the key assembly features, and trace and control the corresponding deviation assembly nodes. Finally, as a typical complex multi-stage process, the diesel engine assembly system is used for validation. Through experimental comparison with actual assembly processes, the results show that this method can effectively model the deviation flow in multi-stage assembly processes, identify key quality factors, and achieve deviation traceability. © 2024 IEEE.

关键词： Assembly machines

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A Self-organized Coverage Algorithm for UAV Swarms based on Color Information Recognition 4

A Self-organized Coverage Algorithm for UAV Swarms based on ...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Liu, Tao Zhang, Xiao Zhang, Junfu Jiang, Shiqi School of Mechanical Engineering Xi Hua University Chengdu China International Joint Institute of Robotics and Intelligent System School of Automation Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541729

This paper proposes a self-organized coverage method for unmanned aerial vehicle (UAV) swarm aimed at multi-area coverage. The proposed method attempts to take the color light emitted from the UVA as an important state information. The virtual forces and the color state are incorporated to develop the self-organized coverage algorithm, so that the communication dependency among UAV swarm has been weakened. Simulation experiments show that under the condition of weak communication, the algorithm effectively achieves coverage of multiple areas. Moreover, when part of UAVs in the swarm are damaged or fails, the swarm based on the proposed algorithm can achieve self-organized recovery and continue to complete the task. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Utilizing the Entropy Weighting Method to Determine Objective Weights in Robot Trajectory Optimization 6

Utilizing the Entropy Weighting Method to Determine Objectiv...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Shen, Nanyan You, Hua Li, Jing Qian, Hui School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China SHANGHAI-FANUC Robotics CO.LTD General Manager's Office Shanghai China

ISBN: (纸本)9798350353174

Optimizing trajectories of collaborative robots is crucial to ensure the efficiency and safety of human-robot interactions in many manufacturing industries. This paper introduced an entropy weighting-based method for determining objective weights in trajectory optimization. Virtual simulations were conducted obtaining datasets of robot trajectory optimization so that, the application scope of the proposed weight determination method could be expanded. The validity of this method was affirmed through comparative simulation experiments. The results demonstrated that in maneuvers in the negative z-axis direction, optimized weights significantly outperformed baseline weights in terms of energy consumption and nominal trajectory tracking, with scores of 0.97 versus 0.68, and 0.82 versus 0.54, respectively. This indicates a better performance than subjective weight setting method in enhancing the performance of collaborative robot trajectory optimization. © 2024 IEEE.

关键词： Trajectories

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Highly Sensitive Flexible Resistance Strain Sensor Based on PDMS and Silver Nanowire 3

Highly Sensitive Flexible Resistance Strain Sensor Based on ...

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3rd International Conference on Computer, Control and robotics, ICCCR 2023

作者： Sun, Weiming Liu, Mei Djoulde, Aristide Liu, Xinyue School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai200444 China

ISBN: (纸本)9781665492126

Flexible sensing film has a broad application prospect in the fields of medicine, wearable electronic devices, complex environment sensing, and others. In this paper, dimethylsiloxane (PDMS) was used as a flexible substrate to prepare composite conductive films by covering silver nanowires (AgNWs) with specific lines on the substrate surface. The resistance change of the sensor is mainly caused by the change in the number of contact points between AgNWs on the film surface, which has higher linearity than the traditional resistance strain gauge. The sensor is very sensitive and has a very good measuring effect on the pressure of 0-6N and the micro-bending caused by 0-1N. Common sensors made of micro-nano materials are usually affected by transverse strain. The sensor in this experiment designs a special circuit structure, which can effectively avoid the lateral effect. The sensor also has the characteristic of self-recovery, which can achieve self-recovery under a certain degree of damage. It can be used to detect small changes in the skin. © 2023 IEEE.

关键词： Substrates

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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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Fusing Dynamics and Reinforcement Learning for Control Strategy: Achieving Precise Gait and High Robustness in Humanoid Robot Locomotion 23

Fusing Dynamics and Reinforcement Learning for Control Strat...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Zhao, Zida Huang, Haodong Sun, Shilong Li, Chiyao Xu, Wenfu Harbin Institute of Technology Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and Automation Shenzhen518055 China

ISBN: (纸本)9798350373578

Achieving precise gait planning and high robustness in locomotion control is crucial for the development and application of humanoid robots. In this paper, a novel control strategy is proposed, which combines dynamics control and reinforcement learning (RL), leveraging the precision of dynamics control and the robustness of RL. Specifically, foot placements for each step of the humanoid robot are designed, and the trajectories of the center of mass (CoM) and feet are obtained using a 3D linear inverted pendulum model (3D LIPM). Subsequently, joint angles during motion are calculated based on the trajectories of the CoM and feet using inverse kinematics equations. Finally, the obtained joint angles are trained as baseline actions using RL algorithms. To enhance control robustness, parameter domain randomization is introduced during the training process. By employing this control strategy, simulations of various single-step gaits, such as walking forward, walking to the right, and making right turns, are achieved. Additionally, trajectory tracking, locomotion tests on different terrains, and disturbance resistance are conducted. The simulation results demonstrate that the proposed control strategy enables precise gait control and exhibits strong robustness in humanoid robots. © 2024 IEEE.

关键词： Inverse kinematics

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Highly Compressible and Stretchable Piezoresistive Sensor Based 3D Graphene-Melamine Composite Foam for Gait Motion Detection 16th

Highly Compressible and Stretchable Piezoresistive Sensor Ba...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Owusu, Eric Kwame Djoulde, Aristide Jiang, Zhen Liu, Mei Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (纸本)9789819964857

High-performance piezoresistive sensors that are stretchable, compressible, and ultralight have fueled significant interest in gait motion detection. However, developing a highly sensitive, low-cost sensor with ultralow detection and excellent mechanical stability for sensitive body flexion monitoring is a significant challenge. To address the aforementioned challenges, a stretchable and compressible piezoresistive pressure sensor is fabricated by embedding multilayer graphene (Gr) nanoparticles into the porosity of melamine foam (MF). The graphene melamine nanocomposite foam (GMNCF) is prepared by soaking the MF in a dissolved dispersion of Gr. The as-fabricated GMNCF piezoresistive sensors featured sensitivities ranging between, 0.11–8.21 kPa−1 in pressure ranges between 0–0.03 kPa, as well as excellent durability over a long period of time (1000 s). The device can effectively detect gait motions such as jogging, walking, running, heel striking, foot flat, and toes-off due to its strong piezoresistive sensing capabilities. Our results showcase the potential application of GMNCF, as a wearable device for monitoring human motions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Graphene

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Wearable Piezoresistive Pressure Sensor Based on Graphene and PDMS Nanocomposite Sponge for Foot Steps Detection 3

Wearable Piezoresistive Pressure Sensor Based on Graphene an...

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3rd International Conference on Computer, Control and robotics, ICCCR 2023

作者： Owusu, Eric Kwame Djoulde, Aristide Liu, Mei Zhen, Jiang School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai200444 China

ISBN: (纸本)9781665492126

Wearable sensors based on nanocomposite sponge are gaining popularity due to their high sensitivity, stretchability and offer new opportunities for applications ranging from finger motion detection to soft robotics. However, when designing skin-Adaptable, mountable, or non-invasive wearable devices, several features need to be taken into account, such as stretchability, flexibility, sensitivity, and biocompatibility. Herein, we present a facile, cost-effective, flexible, and stretchable piezoresistive pressure sensor based on a graphene/polydimethylsiloxane (PDMS) nanocomposite sponge (GPNCS) for finger motion detection. The maximum sensitivities were 133.95 kPa-1 for the pressure range of 1-10 N and 5.05 kPa-1 for the pressure range of 10-20 N respectively. When various pressures are applied, the pressure response of the devices are highly stable, repeatable, and reversible. Moreover, the piezoresistive pressure sensor has excellent sensing performance at various pressures ranging from 1 to 20 N and is mechanically robust enough to work under repeated load without degradation. The proposed device demonstrated an exceptional ability to monitor small and large finger motion, which has numerous applications in the range of finger motion detection, flexible insole as wearable healthcare monitoring and gait monitoring device © 2023 IEEE.

关键词： Graphene

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Performance Analysis of Deepfake Text Detection Techniques on Social-media 2

Performance Analysis of Deepfake Text Detection Techniques o...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Gaur, Adamya Singh, Sanjay Kumar Saxena, Pranshu University School of Automation & Robotics Guru Gobind Singh Indraprastha University Delhi India Abes Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350382952

In the last few years, there has been remarkable progress in the domain of natural language generation & understanding. This has led to the development of enhanced text generation capability of machines that can generate spurious text or deepfake text. The technology may be exploited to change and shape public sentiment on social media. Therefore, mechanisms to detect deepfake text is a crucial task. The study emphasizes on detection of deepfake text on social media platform X (formerly Twitter). It presents the performance of machine learning classifiers Logistic regression (LR), XGB classifier, and AdaBoost classifier, utilizing feature engineering methods Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF). Deep learning methods such as Dense Neural Networks (DNN), Bi-directional Long Short-Term Memory (Bi-LSTM), and Convolutional Neural Networks (CNN)in addition to state-of-the-art models and variants such as Bi-Directional Encoder Representations from Transformers (BERT), DistilBERT & RoBERTa are also a part of study, leading to 89.17% accuracy in deepfake text detection. © 2024 IEEE.

关键词： Long short-term memory

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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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