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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ganesh, L. Venkatesh, R. Prasanna Kumar, Kaushal Kumari, Khushboo Borkakoty, Sangeeta Raju, A. Rajalakshmi Engineering College Robotics and Automation Dept. Tamilnadu Chennai India SACS MAVMM Engineering College Department of Mechanical Engineering Tamilnadu Madurai India School of Life Sciences Department of Botany Mahatma Gandhi Central University Bihar India University of Science & Technology Department of Computer Science & Electronic Meghalaya India KLEF Vaddeswaram Green Field Department of Commerce Vijayawada India

ISBN: (纸本)9798350304480

This paper proposes using good sized getting to know fashions to recognize lesions of cotton leaves in the context of pictures of the reaping within the subject. Cotton is among the maximum economically essential plant harvests, as it's far grown all over the international. Its spread in tropical areas has made it the goal of a wide variety of farming irritations and illnesses, necessitating succesful plans. moreover, the consequences of commonplace disturbances and disorders are difficult to isolate inside the early stages, and the author may additionally battle to locate the suitable visible verification of a scientific difficulty. To help in resolving the difficulty, it contemporary inquiry proposes a solution that takes into consideration substantial training within the filtering of cotton leaves, making it possible to reveal the cotton yield's health and make higher judgments approximately its management. The studying models GoogleNet like Resnet50, which use convolutional cerebrum associations, performed an accuracy of 86.6 percentage and 89.2 percentage, respectively. assist vector machines, Closest k-pals, faux creativeness associations, and neuro-tender are some of the extraordinary as well as everyday tactics again for treatment of photographs. The convolutional cerebellum networks ended up being 25% extra unique, implying that this method can upload to a more strong and short investigation of the flora looking to fill inside the field. © 2023 IEEE.

关键词： Artificial Intelligence Convolutional Neural Networks Image Processing Precision Agriculture

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Acquiring feedback into the manufacturing process design from Industrial policies

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Procedia CIRP 2024年 128卷 579-584页

作者： Alexios Papacharalampopoulos Manolis Toupalis Panagiotis Lagios Olga Maria Karagianni Panagiotis Stavropoulos Laboratory for Manufacturing Systems and Automation (LMS) Department of Mechanical Engineering and Aeronautics University of Patras Rio Patras 26504 Greece Gizelis Robotics Schimatari Viotias 32009 Kormatzini Area Greece

The regulatory and social framework of European industry can be demanding, i.e. in terms of achieving different strategies, such as Industry 4.0 and Industry 5.0. To study this co-existence of strategies, a circular cell is utilized herein. It has been implemented based on a specific design, meeting requirements for an automotive microfactory and under the concept of circular economy. The objective of the cell has been to address resources efficiency and partial digitalization of workflows towards human-in-the-loop remanufacturing parts of electric vehicles. The process chain includes removal and joining processes, the processed part has been of medium complexity, and the workflow has engaged several digital tools, as a result of the quality monitoring requirements. The cell has been operational and various qualitative metrics have been elaborated. Subsequently, feedback is extracted towards the design for future applications pertaining to both circular economy and Industry 5.0. As such, in addition to the environmental sustainability implied by the resources efficiency, the production resilience and the human-centricity of the jobs involved are also regarded as extra requirements. Also, the connection to the ecosystem is taken into consideration. Finally, guidelines for future designs are also extracted.

关键词： Manufacturing process Cell Industry 5.0 application design criteria

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An Improved Method for Identifying SSVEP Single-Channel Target Frequency for BCI Systems 3

An Improved Method for Identifying SSVEP Single-Channel Targ...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Wahab, Adeel Khan, Umar Shahbaz Nawaz, Tahir Akbar, Hassan Shah, Syed Tayyab Hussain Khan, Zulfiqar Hasan Department of Mechatronics Engg National University of Sciences and Technology IslamabadH-12 Pakistan National Centre of Robotics and Automation National University of Sciences and Technology IslamabadH-12 Pakistan Department of Basic Sciences and Humanities National University of Sciences and Technology IslamabadH-12 Pakistan Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

ISBN: (纸本)9798331504861

Steady-state visual evoked potential (SSVEP) is a brain response to flickering visual stimuli in the occipital area. SSVEP detection methods usually utilize multichannel configurations, which can be intricate and uncomfortable. Conversely, single-channel configurations are simple and user-friendly, but attaining the necessary accuracy is difficult, requiring the development of a reliable and accurate single-channel technique. For this purpose, this study introduces a hybrid VMD-LGBM approach to single-channel SSVEP detection. The proposed technique utilizes VMD as a preprocessor that decomposes the SSVEP signal into harmonic, intrinsic mode functions (IMFs), and noisy IMFs. To separate harmonic IMFs from noisy IMFs, the crest factor of the cross-correlation function was introduced. Then the restored SSVEP signal is used to extract features using power spectral density analysis (Welch's approach), followed by classification using a Light Gradient Boosting Machine. Our proposed approach performs better than available techniques in terms of classification accuracy and information transfer rate. The proposed single-channel approach provides better efficiency and user-friendliness for practical applications. ©2024 IEEE.

关键词： Harmonic analysis

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Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement

Simultaneous Estimation of Shape and Force along Highly Defo...

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IEEE International Conference on robotics and automation (ICRA)

作者： Yiang Lu Bin Li Wei Chen Junyan Yan Shing Shin Cheng Jiangliu Wang Jianshu Zhou Qi Dou Yun-Hui Liu Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270° and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space without interactions as well as constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods.

关键词： Optical fiber sensors Shape Soft sensors Force Estimation Robot sensing systems Fiber gratings

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Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement

arXiv

引用

arXiv 2024年

作者： Lu, Yiang Li, Bin Chen, Wei Yan, Junyan Cheng, Shing Shin Wang, Jiangliu Zhou, Jianshu Dou, Qi Liu, Yun-Hui T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270◦ and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space without interactions as well as constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

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Stiffness-Based Hybrid Motion/ Force Control for Cable-Driven Serpentine Manipulator*

Stiffness-Based Hybrid Motion/ Force Control for Cable-Drive...

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IEEE International Conference on robotics and automation (ICRA)

作者： Wenshuo Li Wenfu Xu Peisheng Huang Boyang Lin Bin Liang School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In recent years, there has been a growing demand for robotic manipulators to perform tasks in various unstructured environments and situations requiring precision and force control. However, traditional robotic arms have limitations in fully leveraging their advantages in such scenarios. To address this demand, we have designed a cable-driven serpentine manipulator (CDSM) that combines force and precision motion control. This control method allows for precise manipulation of forces and torques at the end-effector, particularly in applications like electric vehicle charging and narrow-space exploration. It also enables independent control in multiple configurations. We achieve force-position hybrid control in task space, ensuring accurate control of end-effector force while achieving precise position control in other directions. Additionally, we implement joint angle closed-loop control in joint space to reduce the impact of cable elasticity deformation and friction on joint motion accuracy. Finally, servo control is applied at the lowest motor level. This paper investigates the modeling, sensing, and control of CDSM within a unified framework of hybrid motion/force control. Through experiments and simulations, we demonstrate the high accuracy and practicality of this control method in various scenarios.

关键词： Accuracy Force Aerospace electronics Arms End effectors Sensors Task analysis

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A Dog Harness Product Design Assessment Using EDAS Method 23th

A Dog Harness Product Design Assessment Using EDAS Method

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Vadivel, S.M. Alli, P. Vinoth Babu, N. Kumar, Vimal Abhinav, A. VIT Business School Chennai Vandalur Kelambakkam Road Chennai600127 India Department of Robotics and Automation Rajalakshmi Engineering College Thandalam Chennai602105 India Chaoyang University of Technology Taichung County41349 Taiwan VIT Law School Vandalur Kelambakkam Road Chennai600127 India

ISBN: (纸本)9783031648496

A smart dog harness is a wearable device that integrates technology to enhance the safety, health, and well-being of dogs. This device incorporates various features such as GPS tracking, activity monitoring, and health sensors that can provide pet owners with real-time data about their dog’s movements, health status, and behavior. The smart dog harness is designed to be comfortable, durable, and easy to use, with features like LED lights for visibility in low light conditions, a panic button that alerts the owner if the dog is lost, and an app that allows the owner to monitor the dog’s activity remotely. The technology used in a smart dog harness is rapidly evolving, and as more pet owners seek ways to enhance their dog’s care, it is likely that these devices will become increasingly popular. Ultimately, a smart dog harness can be an invaluable tool for any pet owner who wants to keep their furry friend safe, healthy, and happy. Here we are applying some product design techniques like prototype design models analyze using EDAS method for overall designing a smart dog harness. For that purpose, we have analyzed comfortability, durability, ease of use, price, health monitoring, safety features and behavioral feedback under each existing and our product prototype model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Product design

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Human-Like Robot Impedance Regulation Skill Learning from Human-Human Demonstrations

arXiv

引用

arXiv 2025年

作者： Li, Chenzui Wu, Xi Liu, Junjia Teng, Tao Chen, Yiming Calinon, Sylvain Caldwell, Darwin Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa16163 Italy

Humans are experts in collaborating with others physically by regulating compliance behaviors based on the perception of their partners’ states and the task requirements. Enabling robots to develop proficiency in human collaboration skills can facilitate more efficient human-robot collaboration (HRC). This paper introduces an innovative impedance regulation skill learning framework for achieving HRC in multiple physical collaborative tasks. The framework is designed to adjust the robot compliance to the human partner’s states while adhering to reference trajectories provided by human-human demonstrations. Specifically, electromyography (EMG) signals from human muscles are collected and analyzed to extract limb impedance, representing compliance behaviors during demonstrations. Human endpoint motions are captured and represented using a probabilistic learning method to create reference trajectories and corresponding impedance profiles. Meanwhile, an LSTM-based module is implemented to develop task-oriented impedance regulation policies by mapping the muscle synergistic contributions between two demonstrators. Finally, we propose a whole-body impedance controller for a human-like robot, coordinating joint outputs to achieve the desired impedance and reference trajectory during task execution. Experimental validation was conducted through a collaborative transportation task and two interactive Tai Chi pushing hands tasks, demonstrating superior performance from the perspective of interactive forces compared to a constant impedance control method. Copyright © 2025, The Authors. All rights reserved.

关键词： Electromyography

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Multi-Objective Optimization Design for DC-Link Capacitor of Motor Driver

Multi-Objective Optimization Design for DC-Link Capacitor of...

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2024 CPSS and IEEE International Symposium on Energy Storage and Conversion, CPSS and ISESC 2024

作者： Liu, Qian Huang, Kangxing Wei, Xueliang Zheng, Yihong Xiang, Hao School of Automation Wuhan University of Technology Wuhan China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong College of Artificial Intelligence China University of Petroleum-Beijing Beijing China Wuhan IActuator Robotics Co. Ltd Software Development Department Wuhan China

ISBN: (纸本)9798350380514

The capacitor is a common weak point in motor drive system design, necessitating targeted selection, design, and optimization of capacitor banks within these systems. Traditional capacitor design often relies on engineering experience, usually meeting specific capacitance and voltage requirements through simple parallel or series connections of capacitors. This approach often fails to fully optimize volume, cost, and reliability. This paper uses comprehensive theoretical modeling to optimize capacitor module design, effectively balancing capacitor lifespan, cost, and volume. For the design and selection of DC-link capacitors in a 1kW permanent magnet synchronous motor drive system, a multi-objective optimization design method for capacitor banks is proposed. The study treats the individual capacitance values of parallel capacitors as variables, significantly enhancing the overall performance of the capacitor module through precise optimization and the introduction of necessary constraints in the selection design. The proposed method is validated with a 1kW permanent magnet synchronous motor drive system. The optimization results show that under the guidance of a multi-objective optimization framework, the designed DC-link capacitor module meets physical size requirements while achieving overall optimization in terms of volume, cost, and lifespan, significantly improving system reliability. © 2024 IEEE.

关键词： Permanent magnets

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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