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Minimum-Time and Minimum-Jerk Gait Planning in Joint Space for Assistive Lower Limb Exoskeleton

Journal of Bionic engineering 2023年第5期20卷 2164-2178页

作者： Habib Mohamad Sadjaad Ozgoli Fadi Motawej Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran14117-13116Iran Department of Mechatronics Engineering Tishreen UniversityLattakia0096341Syria

Assistive lower limb exoskeleton robot has been developed to help paraplegic patients walk again.A gait planning method of this robot must be able to plan a gait based on gait parameters,which can be changed during the stride according to human intention or walking *** gait is usually planned in cartesian space,which has shortcomings such as singularities that may occur in inverse kinematics equations,and the angular velocity of the joints cannot be entered into the ***,it is vital to have a gait planning method in the joint *** this paper,a minimum-time and minimum-jerk planner is proposed for the robot *** do so,a third-order system is defined,and the cost function is introduced to minimize the jerk of the joints throughout the *** minimum time required is calculated to keep the angular velocity trajectory within the range specified by the motor’s maximum *** conditions of the joints are determined to secure backward balance and fulfill gait ***,the proposed gait planning method is tested by its implementation on the Exoped®exoskeleton.

关键词： Bionic robot Gait planning Optimization Complete paraplegic Lower limb exoskeleton

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Compensating Force Imbalance Using Anchored Beam Coupling Mechanism in MEMS Tuning Fork Gyroscope (TFG) Drive Frame Structure 6

Compensating Force Imbalance Using Anchored Beam Coupling Me...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Bashir, Uzair Bazaz, Shafaat Ahmed Javaid, Faraz Department of Electrical and Computer Engineering Islamabad Pakistan Department of Mechatronics Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

This paper presents an improved tuning fork gyroscope (TFG) structure utilizing two gyroscope drive resonator frames and a diamond-shaped coupling spring mechanism. It explores a two-degree-of-freedom system for the drive mode frame with a coupling mechanism to reduce external vibration sensitivity while keeping angular rate sensitivity. Expressions are derived to study and optimize the coupling spring structure. FEM modeling using ANSYS software validates the expressions that are derived. Simulations were performed to ensure anti-phase motion and avoid structural imbalance induced in-phase motion. Simulations demonstrate compensation for force imbalance and preservation of anti-phase motion. FEM modal analysis confirms a significant frequency separation of 10.90 kHz between in-phase and anti-phase resonant frequencies, with values at 6.81 kHz and 17.713 kHz, respectively. The improved frequency separation in TFG can ensure robustness against external environmental vibrations when fabrication imperfections occur and ensure better reliability for the measurement of angular velocity in inertial sensing applications. © 2024 IEEE.

关键词： Vibration analysis

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Utilization of genetic algorithm in tuning the hyper-parameters of hybrid NN-based side-slip angle estimators

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Neural Computing and Applications 2024年第30期36卷 19055-19074页

作者： Essa, Mohamed G. Elias, Catherine M. Shehata, Omar M. Research Group Cairo Egypt Mechatronics Department Faculty of Engineering and Materials Science German University in Cairo Cairo Egypt Computer Science Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Mechatronics Department Faculty of Engineering Ain Shams University Cairo Cairo Egypt

This paper proposes a solution to enhance and compare different neural network (NN)-based side-slip angle estimators. The feed-forward neural networks (FFNNs), recurrent neural networks, long short-term memory units (LSTMs), and gated recurrent units are investigated. However, there is a lack in the selection criteria of the architectures’ hyper-parameters. Therefore, the genetic algorithm is integrated with the NN-based estimators to find the optimal hyper-parameters for the studied architectures. The tuned hyper-parameters in this work include the number of neurons, number of layers, activation function, optimizer type, and learning rate. The objective function of the optimization problem is minimizing the root-mean-square error (RMSE) on multiple testing data. The optimal models are further included in the design of a hybrid NN estimator with Kalman filter. In the hybrid estimators, the optimal NN estimators are used as virtual sensors to correct the prediction of the side-slip angle resulting from the mathematical lateral vehicle model. Eventually, the performance of the best selected model is evaluated in terms of different metrics;mean RMSE, mean error variance, mean training time, and mean estimation time. LSTMs are found to achieve the lowest mean RMSE while being tested on highly generalized data yielding the highest training and estimation time. However, FFNNs achieve the lowest RMSE while being tested on low generalized data and the lowest training and estimation time. Meanwhile, it is observed that the hybrid estimators achieved lower RMSE with great enhancement compared to the non-hybridized ones proving the effectiveness of the proposed approach and increasing the side-slip estimation generalization ability in unknown environments with high uncertainties, which are not covered by the training dataset for the NNs estimators. © The Author(s) 2024.

关键词： Kalman filters

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Adaptive Imitation Learning for End-to-end Enhanced Control of Seven-Axis Manipulators 30

Adaptive Imitation Learning for End-to-end Enhanced Control ...

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30th International Conference on mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Wang, Boran Wu, Han Department of Computer Science and Technology Shenzhen China Department of Mechatronics Engineering Shenzhen China

ISBN: (纸本)9798350391916

The rapid advancement of industrial automation has underscored the significance of sophisticated control mechanisms for robotic manipulators, especially seven-axis manipulators, known for their flexibility and capability in handling complex tasks. Traditional control methods often struggle with adaptability and managing nonlinear, uncertain environments. To address these challenges, this paper proposes an adaptive imitation learning-based control strategy for seven-axis manipulators. First, the adaptive imitation learning algorithm is introduced and its applicability to manipulator control is analyzed. Then, a reinforcement learning based model of the seven-axis manipulator is established and simulated in a virtual environment to verify the algorithm's effectiveness. Finally, the limitations of the adaptive imitation learning algorithm in the context of manipulator control are discussed, and future research directions are outlined. This study not only offers a novel control strategy for seven-axis manipulators but also serves as a reference for the broader application of adaptive imitation learning algorithms in various domains. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploratory Analysis of Zero-Shot Lunar Crater Detection using SAM for Chandrayaan-2 Images 3

Exploratory Analysis of Zero-Shot Lunar Crater Detection usi...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Koranglekar, Kaustubh Prateek, K. Kumar, Shankar Biswas, Dheeraj Kumar Khanna, Nitin Department of Mechatronics Bhilai India Department of Electrical Engineering Bhilai India Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798331528201

This paper explores the application of a zero-shot lunar crater detection method based on the Segment Anything Model (SAM) for high-resolution Digital Terrain Model (DTM) images from ISRO's Chandrayaan-II mission. A pre-trained transformer-based instance segmentation model, SAM, is used to segment and identify lunar craters without requiring training data. The proposed method is capable of detecting craters in varying crater sizes and shapes, highlighting its potential for generalizable planetary feature detection. This work investigates the applicability of general-purpose deep learning models in planetary exploration and offers insights for efficient lunar surface analysis. © 2024 IEEE.

关键词： Lunar surface analysis

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RetinaFusion: A Comprehensive Ensemble for Diabetic Eye Care 4

RetinaFusion: A Comprehensive Ensemble for Diabetic Eye Care

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Gupta, Prayash Ujjwal Selvin Paul Peter, J. Priya Esther, B. Srmist Department of Computer Science and Engineering Chennai India Srmist Department of Mechatronics Chennai India

ISBN: (纸本)9798350393354

Given the increasing worldwide occurrence of diabetes, which is expected to affect 9.9% of the world's inhabitants by 2045, the need for efficient chronic eye treatments has become even more urgent. This unique ensemble technique tackles the complex issues posed by diabetic retinopathy (DR). By utilizing state-of-the-art equipment instruction and models using deep learning, it showcases exceptional precision in identifying indications of diabetic retinopathy (DR), such as microaneurysms, hemorrhages, and retinal edema. The system's incorporation of clinical data enhances its ability to make predictions, making it easier to implement early intervention techniques. By doing thorough validation, this tool emerges as a powerful solution that has the potential to greatly reduce the burden of retinopathy from diabetes on world health. By actively participating in the proactive handling of diabetic retinopathy (DR) and providing a source of optimism for persons who are at danger of permanent vision loss, we are leading the way in enhancing diabetic eye care. We will use plasma reports and digital retinal images to reliably assess the severity of these conditions. To accomplish this, we initially ascertain the presence or absence of diabetes in the patient. The utilization of the K Nearest Neighbors machine learning classification technique has facilitated this achievement. This offers a more positive future for those affected by this widespread consequence of diabetes. © 2024 IEEE.

关键词： Eye protection

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FaceForge: Sculpting Realistic Faces from Detailed Textual Prompts 4

FaceForge: Sculpting Realistic Faces from Detailed Textual P...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Thakur, Umang Kalantri, Raj Paul Peter, J. Selvin Esther B, Priya Srmist Department of Computer Science and Engineering Chennai India Srmist Department of Mechatronics Chennai India

ISBN: (纸本)9798350307757

Within the domain of generative adversarial networks (GANs), notable progress has been made in generating lifelike visuals based on textual stimuli. Nevertheless, the primary emphasis has centered on basic assignments such as producing visual representations of flowers using textual descriptions. This work aims to explore the unexplored field of face generation, specifically focusing on generating faces from detailed textual descriptions such as "A person with wavy hair, an oval face, and a moustache."Motivated by the potential influence on important applications such as criminal face reconstruction, we address the problem of limited datasets by developing an algorithm that automatically converts. We utilise the advanced DC-GAN with GAN-CLS loss to address the complexities arising from subtle information and varied caption lengths. In order to enhance the resilience of the training process, we propose the incorporation of label inversion for both real and synthetic images, along with the introduction of noise into the discriminator. © 2024 IEEE.

关键词： Generative adversarial networks

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ForgeryDetect: AI-driven Deep Fake Identification

ForgeryDetect: AI-driven Deep Fake Identification

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Bharti, Ayush Sinha, Vishal Kumar Peter, J. Selvin Paul Priya Esther, B. Srmist Department of Computer Science and Engineering Chennai India Srmist Department of Mechatronics Chennai India

ISBN: (纸本)9798350309140

The rise of deepfake videos presents substantial obstacles in guaranteeing the genuineness and reliability of visual media material. This study focuses on the crucial problem of identifying deepfake content by introducing a sophisticated method that combines ResNext CNNs along with LSTM models, utilizing transfer learning approaches. Employed a comprehensive approach by using a wide array of datasets such as FaceForensics++, Celeb-DF, and the Deepfake Detection Challenge. Utilizing transfer learning, a ResNext CNN that has been pre-trained is employed to extract complex features from video frames. This enables the subsequent training of an LSTM layer to identify temporal patterns and distinctive cues that are exclusive to modified videos. The effectiveness of fusion model has been demonstrated through extensive experimentation and validation on these rich datasets. It demonstrates a strong ability to reliably distinguish between authentic and manipulated movies, regardless of the different facial alteration techniques, environmental variables, and complex situations seen in real-life situations. The integration of ResNext-based feature extraction with LSTM-based temporal modeling shows substantial progress in the detection of deepfake videos. The efficacy of approach is highly promising, since it provides both dependability and scalability. This study makes a significant contribution to the field of deepfake identification by offering a powerful collection of techniques to tackle the growing concerns surrounding disinformation and manipulation in visual media. © 2024 IEEE.

关键词： Convolutional neural networks

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A Framework for Industrial Inspection System using Deep Learning

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Annals of Data Science 2024年第2期11卷 445-478页

作者： Hridoy, Monowar Wadud Rahman, Mohammad Mizanur Sakib, Saadman Department of Mechatronics & amp Industrial Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh Department of Mechanical Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh Department of Computer Science & amp Engineering Chittagong University of Engineering & ampTechnology Chattogram Bangladesh

Industrial Inspection systems are an essential part of Industry 4.0. An automated inspection system can significantly improve product quality and reduce human labor while making their life easier. However, a deep learning-based camera inspection system requires a large amount of data to classify the defective products accurately. In this paper, a framework is proposed for an industrial inspection system with the help of deep learning. Additionally, A new dataset of hex-nut products is proposed containing 4000 images, i.e., 2000 defective and 2000 non-defective. Moreover, different CNN architectures, i.e., Custom CNN, Inception ResNet v2, Xception, ResNet 101 v2, ResNet 152 v2, are experimented with the concept of transfer learning on the new hex-nut dataset. Fine-tuning the CNN architectures is performed by freezing the last 14 layers, which provided the optimal architecture, i.e., Xception (last 14 layers trainable, excluding the fully connected layer). The proposed framework can efficiently separate the defective products from the non-defective products with 100% accuracy on the hex nut dataset. Furthermore, the proposed optimal Xception architecture has experimented on a publicly available casting material dataset which produced 99.72% accuracy, outperforming existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Industry 4.0

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i-Glove: An Intelligent Glove System Based on Deep Learning to Support Deaf-blind Individuals in Recognizing Banknotes 3

i-Glove: An Intelligent Glove System Based on Deep Learning ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain, Fahad Mohammad Alavi, Rafit Safat, Yeamin Sarker, Gobinda Chandra Noor, Nafisa Hossain, Ekram Mymensingh Engineering College Department of Computer Science and Engineering Mymensingh Bangladesh University of Dhaka Department of Robotics and Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The deaf-blind community faces significant challenges in identifying banknotes due to simultaneous hearing and vision impairments, which complicate their ability to conduct daily financial transactions. Recent advances in Artificial Intelligence (AI)-based assistive technologies offer promising potential to address these challenges. This study presents the "i-Glove,"a wearable device specifically developed to assist the deaf-blind community in Bangladesh with accurate banknote identification. The i-Glove is designed to recognize nine common Bangladeshi banknotes (e.g., BDT 20, 50, 100) through a camera module integrated on the palmar side of the glove, which captures real-time images of the banknotes. These images are then processed by a convolutional neural network (CNN) model to accurately classify each banknote. Identified notes are translated into unique vibration patterns that are delivered to the user through a shaftless vibration motor embedded in the glove, providing intuitive, non-visual communication. Compared to previous approaches, our model demonstrates superior performance even with a compact dataset, achieving an overall F1-score accuracy of 96.55%, with class-specific precision, recall, and F1-score metrics ranging from 90% to 100%. Additionally, a dedicated application is developed which supports real-time banknote detection and interfaces seamlessly with the glove. The proposed system represents a significant step forward in assistive technology for the deaf-blind community, empowering the users with greater autonomy in financial transactions. © 2024 IEEE.

关键词： Convolutional neural networks

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