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检索条件"机构=Laboratory of Robotics Informatics and Complex Systems"

共 51 条记录，以下是11-20 订阅

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Exploring Neural Networks for Forward Kinematics of the Robotic Arm with Different Length Configurations: A Comparative Analysis

Exploring Neural Networks for Forward Kinematics of the Robo...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli Higher Institute of Information and Communication Technologies University of Carthage RISC Laboratory (LR16ES07) Borj Cedria Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia

Our work investigates the utilization of Artificial Neural Networks (ANNs) to address the complexities associated with Forward Kinematics (FK) problems within the field of robotics. We undertake an extensive comparative analysis to assess how ANNs perform under different circumstances with robotic arms of varying lengths and impact the overall system’s functionality. The training, testing, and validation of ANNs are carried out using MATLAB for a simulated 2-DoF serial robotic arm involving three distinct datasets: fixed step size, random step size, and sinusoidal step size. Three training optimizers, namely Levenberg Marquardt (LM), Bayesian Regularization (BR), and Stochastic Conjugate Gradient (SCG), are considered within the ANN architecture. Based on Mean Square Error (MSE) values, the numerical findings reveal the potential of ANN in estimating forward kinematic solutions of complex robotic manipulators with different arm lengths and reducing computational complexity.

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Enhanced Diagnosis of Lithium-Ion Battery Health in Electric Vehicles Through Advanced Hybrid Deep Learning Model Incorporating Attention Mechanism

Enhanced Diagnosis of Lithium-Ion Battery Health in Electric...

引用

Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Walid Mchara Mohamed Abdellatif Khalfa Lazhar Manai Laboratory of Robotics Informatics and Complex Systems (RISC) National Engineering School of Tunis (ENIT) University of El Manar Le Belvedere Tunis Higher Institute of Information and Communication Technologies Hamam Chatt Tunisia CRETn LaNSER Laboratory Borj-Cedria RISC Le Belvédère Tunis Tunisia ENIT University of El Manar

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

With the rising adoption of lithium-ion batteries(LIBs) in electric vehicles, significant research efforts have been dedicated to ensuring the safe and reliable performance of Battery Management systems (BMS). Accurately estimating the State of Health (SOH), a crucial aspect of BMS functionality, is imperative for the secure operation of electric vehicles. To tackle this challenge, we propose a time series model designed specifically for forecasting the State of Health (SOH) of Li-ion batteries. Our approach integrates a CNN with a Long Short-Term Memory (LSTM) architecture, augmented by an attention mechanism. Experimental findings demonstrate that this LSTM-based approach significantly enhances prediction accuracy. The outcomes of this investigation present a promising avenue for improving the dependability and efficiency of electric vehicle battery systems, ultimately advancing the widespread adoption and sustainability of electric transportation.

关键词： Lithium-ion batteries Deep learning Attention mechanisms Time series analysis Transportation Predictive models Prediction algorithms Trajectory Convolutional neural networks Long short term memory

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Improved Diagnosis of Lithium-Ion Battery Health in Electric Vehicles via a Hybrid Deep Learning Model Incorporating Wavelet Transform and Attention Mechanism

Improved Diagnosis of Lithium-Ion Battery Health in Electric...

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ACS/IEEE International Conference on Computer systems and Applications

作者： Walid Mchara Mohamed Abdellatif Khalfa Lazhar Manai Laboratory of Robotics Informatics and Complex Systems (RISC) National Engineering School of Tunis (ENIT) University of El Manar Le Belvedere Tunis CRETn Photovoltaic Laboratory (LPV) Higher Institute of Information and Communication Technologies Borj-Cedria Higher Institute of Information and Communication Technologies ENIT University of El Manar Hamam Chatt Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

As the adoption of lithium-ion batteries (LIBs) in electric vehicles increases, ensuring their reliability and safety is essential. The Battery Management System (BMS) is vital for accurately evaluating the State of Health (SOH) of these batteries to ensure safe vehicle operation. To address this, a novel time series model for predicting SOH in Li-ion batteries is proposed. This model combines wavelet transform with a hybrid architecture of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, enhanced by an attention mechanism to improve performance. Experimental results indicate that the wavelet-enhanced LSTM method significantly improves prediction accuracy. This study presents a promising approach to enhance the reliability and efficiency of electric vehicle battery systems, supporting broader adoption and sustainability in electric transportation.

关键词： Lithium-ion batteries Wavelet transforms Deep learning Attention mechanisms Accuracy Predictive models Prediction algorithms Convolutional neural networks Reliability Long short term memory

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Improved Diagnosis of Lithium-Ion Battery Health in Electric Vehicles via a Hybrid Deep Learning Model Incorporating Wavelet Transform and Attention Mechanism

Improved Diagnosis of Lithium-Ion Battery Health in Electric...

引用

Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Walid Mchara Mohamed Abdellatif Khalfa Lazhar Manai Laboratory of Robotics Informatics and Complex Systems (RISC) National Engineering School of Tunis (ENIT) University of El Manar Le Belvedere Tunis Higher Institute of Information and Communication Technologies Hamam Chatt Tunisia LaNSER Laboratory CRETn Borj-Cedria RISC Le Belvédère Tunis Tunisia ENIT University of El Manar

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

关键词： Wavelet transforms Lithium-ion batteries Deep learning Attention mechanisms Accuracy Predictive models Prediction algorithms Convolutional neural networks Reliability Long short term memory

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Investigation of the Compass Biped Walker's Passive Dynamics and Trajectory Tracking Method for Chaos Control

Investigation of the Compass Biped Walker's Passive Dynamics...

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International Conference on Advanced systems and Electric Technologies (IC_ASET)

作者： Essia Added Hassène Gritli Safya Belghith Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia

The aim of this research is to manage the precise and rhythmic movements of a bipedal robot with a compass configuration as it undergoes a passive dynamic walk. To approximate the walking pattern of a biped robot, we implement an impulsive hybrid nonlinear system. To start with, we showcase the fact that a passive walk by a bipedal compass robot can exhibit unexpected behaviors like bifurcations and chaos with the aid of bifurcation diagrams. In order to suppress such complex nonlinear tendencies, we resort to two methods for regulating the bipedal compass-type robot and achieving a one-periodic gait. The control approach used in this study is based on the passive dynamic walking of the biped robot, and the objective is to follow the trajectory of the period-1 passive movement during the swing phase.

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Investigating Neural Network Hyperparameter Variations in Robotic Arm Inverse Kinematics for Different Arm Lengths

Investigating Neural Network Hyperparameter Variations in Ro...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Borj Cedria Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis EI Manar Le Belvédère Tunis Tunisia

This research delves into the evaluation of Artificial Neural Networks (ANNs) in tackling the intricate domain of inverse kinematics problems within the context of robotic systems. Compared to direct kinematics, inverse kinematics presents significant challenges, especially when dealing with different arm lengths, which can impact the system's overall functionality. In this study, we conduct a comprehensive comparative analysis to assess the performance of ANNs across various robotic arm lengths. Following the forward kinematics of a simple 2-DoF robotic manipulator, three distinct datasets (fixed step size, random step size, and sinusoidal step size) are generated for training, testing, and validation phases. For every dataset, three optimizers, namely Levenberg Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient, are considered with different hyperparameters to investigate the performance of the ANN models. Based on the validation of MSE values, ANN models with different hyperparameters are found to be a promising approach in estimating the inverse kinematic solutions for different arm lengths with data complexities. Such solutions can be further extended to higher DoF robotic solutions and save the computational burden of evaluating analytical solutions.

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Artificial Neural Networks for the Forward Kinematics of a SCARA Manipulator: A Comparative Study with Two Datasets

Artificial Neural Networks for the Forward Kinematics of a S...

引用

Emerging Technologies for Sustainability and Intelligent systems (ICETSIS), ASU International Conference in

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Higher Institute of Information and Communication Technologies University of Carthage Borj Cedria Tunis Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

In the field of robotics and automation, the precise determination of manipulator positions is a fundamental aspect with widespread applications. Despite the significance of analytical methods, solving the Forward Kinematics (FK) for Selective Compliance Assembly Robot Arm (SCARA) manipulator robots can be complex and computationally intensive. Our research introduces a method to address the FK problem for the four degrees of freedom (4-DoF) SCARA manipulator through Artificial Neural Networks (ANNs). We utilize two different generated datasets, one with a fixed step size and another one based on a sinusoidal signal, for training the ANNs. This choice of datasets allows us to assess the ANN s' generalization capabilities across a range of operating conditions. Thereafter, the study is planned to investigate the ANN's performance while employing three distinct training algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Through a comprehensive comparison of the performance of different ANN models, different training algorithms, and the two adopted generated datasets, this study provides valuable insights into selecting the most optimal training configurations for the SCARA robot's FK solutions. We employ the Mean Squared Error (MSE) and the error histogram as two performance metrics to assess the accuracy of various ANNs. The results reveal that optimal MSE outcomes were obtained when utilizing a balanced architecture with three hidden layers in the analysis of the sinusoidal-signal-based datasets.

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Multi-Class Gait Phase Recognition using Machine Learning Models with Two Training Methods

Multi-Class Gait Phase Recognition using Machine Learning Mo...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Amal Mekni Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Borj Cedria Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia Department of Mechanical Engineering Mechtronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ($\sim 91 \%$) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ($\sim 77.7 \%$). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion.

关键词： Training Support vector machines Radio frequency Musculoskeletal system Logistic regression Machine learning algorithms Nearest neighbor methods Sampling methods Random forests Testing

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A Robust LMI-Based Approach for the Position Control of a 1-DoF Knee Rehabilitation Exoskeleton Robot Considering State Constraints

A Robust LMI-Based Approach for the Position Control of a 1-...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Sahar Jenhani Hassène Gritli Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies & RISC Lab (LR16ES07) University of Carthage Tunis Tunisia

This study introduces a robust control strategy designed for a one-degree-of-freedom (1-DoF) knee rehabilitation exoskeleton robot. Focusing on position control, our approach addresses challenges such as state constraints, parameter uncertainties, solid and viscous frictions, and external disturbances. To achieve this goal, we propose an affine state-feedback controller. Additionally, utilizing a quadratic Lyapunov function, we establish some Linear Matrix Inequality (LMI) stability conditions on the matrix gain of the adopted controller to ensure robust stabilization of the robot to the desired position. These conditions are developed using some mathematical tools and congruence transformations. Finally, some results are presented to show the validity and efficiency of the controller in achieving robust stabilization of the 1-DoF knee exoskeleton robot, even under uncertainties and external disturbances.

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Hybrid Deep Learning with Attention Mechanism based Health State Intelligent Diagnosis of Lithium-Ion Batteries

Hybrid Deep Learning with Attention Mechanism based Health S...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Walid Mchara Mohamed Abdellatif Khalfa Lazhar Manai Laboratory of Robotics Informatics and Complex Systems (RISC) National Engineering School of Tunis (ENIT) University of El Manar Le Belvédère Tunis Higher Institute of Information and Communication Technologies Hammam Chatt Tunisia CRETn Borj-Cedria Photovoltaic Laboratory (LPV) RISC Le Belvédère ENIT University of El Manar Tunis Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

With the increasing utilization of lithium-ion batteries in electric vehicles, substantial research has been undertaken to ensure the secure and dependable performance of the Battery Management System (BMS). The precise estimation of State of Health (SOH), a pivotal function of the BMS, is critical for the safe operation of electric vehicles. Addressing this challenge, we present a time series model tailored for predicting the State of Health (SOH) of Li-ion batteries. Our approach combines a CNN with a Bidirectional Long Short-Term Memory (BI-LSTM) architecture, enhanced by an attention mechanism. Experimental results demonstrate that this enhanced LSTM approach notably enhances prediction accuracy. The results from this study offer a promising solution for enhancing the reliability and performance of electric vehicle battery systems, ultimately contributing to the widespread adoption and sustainability of electric transportation.

关键词： Lithium-ion batteries Deep learning Accuracy Time series analysis Transportation Predictive models Prediction algorithms

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