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IEEE Southeastcon

作者： Sean Haque Muhammad Hassan Tanveer Razvan Cristian Voicu Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta Georgia

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Artificial Intelligence (AI) is transforming the landscape of three-dimensional (3D) printing by enabling generative approaches that extend beyond conventional, parameter-tuning strategies. Rather than merely adjusting nozzle temperatures or speeds, modern AI models can create or refine Gcode paths themselves, seeking to balance objectives such as print strength, dimensional accuracy, and defect minimization. This paper introduces a holistic AI-driven workflow for automated Gcode generation. We demonstrate that generative Large Language Models (LLMs) can parse, synthesize, and optimize printing instructions, thus offering an alternative to human-engineered slicing parameters. Our approach incorporates a continuous feedback loop in which a predictive model evaluates each generated Gcode script for potential print outcomes. Experimental results illustrate strong correlations between the number of lines in generated Gcode and key performance metrics (tensile strength, defect rate, and dimensional accuracy). Notably, some scripts with fewer lines reduced material defects yet slightly degraded dimensional accuracy, while more complex scripts improved measurement fidelity but also introduced minor flaws. These findings illuminate the trade-offs in AI-driven 3D printing and highlight the method's capacity for ongoing improvement. By exploring future expansions, such as continuous-function printing trajectories and multi-material prints, this study shows that AI-based Gcode generation can significantly advance 3D printing quality and customization across diverse applications.

关键词： Solid modeling Accuracy Generative AI Large language models Machine learning Predictive models Three-dimensional printing Minimization Trajectory Optimization

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Robust Control of a DC Motor and Load Using Quantitative Feedback Theory 19

Robust Control of a DC Motor and Load Using Quantitative Fee...

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19th IEEE International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, HONET 2022

作者： Moghadam, Amir Ali Amiri Voicu, Razvan Cristian Patel, Kishan Wang, Ying Chang, Yusun Kennesaw State University Department of Robotics & Mechatronics Engineering AtlantaGA United States

ISBN: (纸本)9781665461979

This paper reports on robust position control of a DC motor and gear system attached to a mechanical load. The dynamics of the system will be identified using the sine wave inputs and least square method. It has been found out that a single linear time invariant (LTI) model cannot perfectly predicts the behavior of the system for a range of different frequencies. So, we are using a family of LTI systems instead with parameter uncertainty. Next a robust controller will be designed using Quantitative Feedback Theory (QFT) to provide consistent robust performance for the system. The performance of the QFT controller will be validated through both simulations and experimental data and will be compared with a PID controller. It has been shown that the QFT controller has a superior performance over the PID controller in terms of tracking error. © 2022 IEEE.

关键词： Controllers

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Probabilistic Modelling and Safety Assurance of an Agriculture Robot Providing Light-Treatment 19

Probabilistic Modelling and Safety Assurance of an Agricultu...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Adam, Mustafa Ye, Kangfeng Anisi, David A. Cavalcanti, Ana Woodcock, Jim Morris, Robert Robotics Group Faculty of Science & Technology Norway University of York Dept. of Computer Science United Kingdom Faculty of Engineering and Science Dept. of Mechatronics Norway Saga Robotics As Norway

ISBN: (纸本)9798350320695

Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety-assurance framework can also be used during implementation, and subsequent deployment and operation phases. © 2023 IEEE.

关键词： Agricultural robots

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Lower Limb Joint Torque Estimation via Bayesian Regularized Backpropagation Neural Networks 2

Lower Limb Joint Torque Estimation via Bayesian Regularized ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Narayan, Jyotindra Gritli, Hassène Dwivedy, Santosha K. Indian Institute of Technology Guwahati Mechatronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India National Engineering School of Tunis Le Belvédère Tunis1002 Tunisia

ISBN: (纸本)9798350360523

Prior gait analysis has played a crucial role in assessing overall health and well-being, particularly in individuals of different age groups. Nevertheless, acquiring gait data has necessitated the in-person presence of participants, incurring potential expenses. We introduce a deep learning technique called Bayesian regularized backpropagation neural network (BR-BPNN) to estimate joint torques in lower-limb motion based on anthropometric data and joint movements. The study collects joint movement data from 40 healthy participants (25 males and 15 females) spanning ages 7 to 65, utilizing an established motion-capture system. The input data encompassed various parameters such as age, gender, total body mass, height, thigh length, calf length, ankle-foot length, thigh mass, calf mass, ankle mass, and joint angles for the hip, knee, and ankle. The output data included joint torques calculated for a simplified leg model using the inverse dynamics method derived from the Euler-Lagrangian principle. We presented the conceptual details of the proposed BR-BPNN model and contrast model named Levenberg-Marquardt-based BPNN (LM-BPNN). Performing a comparative analysis, the results revealed that the BR-BPNN model (Rtrain = 0.985, Rtest = 0.955) excelled in accurately estimating joint torques compared to the LM-BPNN model (Rtrain = 0.973, Rtest = 0.924). Furthermore, the BR-BPNN model is found to be effective by 87.19%, 77.87%, and 72.73% in estimating hip, knee, and ankle joint torques for a 51-year-old male subject from the testing dataset. © 2024 IEEE.

关键词： Neural network models

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Seat-Focused Multi-DOF Exoskeleton Wheelchair Synergy for Continuous Lower-Limb Rehabilitation

Seat-Focused Multi-DOF Exoskeleton Wheelchair Synergy for Co...

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IEEE Southeastcon

作者： Razvan Cristian Voicu Charles F Freeman Christopher E Garretson Zoe M Sun Ryan H. Bonus Zachary A Slutzky Niranjan Mukesh Muhammad Hassan Tanveer Yannique Tello Yusun Chang Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta Georgia

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Wheelchair users with limited or nonexistent lower-limb mobility often struggle with significant muscle atrophy, circulatory complications, and other health issues. Although exoskeletons for gait training have shown promise, many are optimized for upright ambulation and overlook individuals remaining fully seated due to clinical or practical constraints. This article presents a seat-focused exoskeleton synergy integrated with a wheelchair to facilitate multi-DOF lower-limb mobilization without requiring transfers or standing maneuvers. The system addresses a critical gap by providing repeated, lower-intensity knee and ankle exercises throughout the day-either passively, actively with assistance, or in a resistive training mode-thus promoting muscle engagement and circulation from a seated position. The exoskeleton leverages the wheelchair's power source and employs modular, adjustable rails to accommodate diverse leg lengths. Emphasis is placed on biomechanical alignment, safe torque control, and user comfort, supported by a model-based controller accounting for inertia, Coriolis effects, and gravity in real time. Bench tests and preliminary trials confirm stable torque output, accurate tracking of desired motion profiles, and minimal burden when attaching or detaching the device. The article introduces modeling for a two-DOF prototype (knee flexion/extension and ankle dorsiflexion/plantarflexion), expandable to include additional joints or degrees of freedom, enabling broader rehabilitation targets such as hip abduction/adduction. By embedding rehabilitative capabilities directly into the wheelchair environment, the proposed approach seeks to close the gap between infrequent clinic appointments and continuous therapy aimed at mitigating muscle ailments. A seat-focused exoskeleton designed for every-day use can facilitate accessible, on-demand rehabilitation and substantially improve long-term health outcomes for mobility impaired users.

关键词： Training Knee Atrophy Ankle Torque Target tracking Wheelchairs Exoskeletons Torque control Muscles

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A three-dimensional compliant bowtie-shaped mechanical amplifier to magnify coaxial displacement in a confined space

A three-dimensional compliant bowtie-shaped mechanical ampli...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jintaek Im Eunsil Jang Cheol Song Department of Robotics and Mechatronics Engineering DGIST Daegu South Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper proposes a novel form of a three-dimensional coaxial bowtie-shaped mechanical amplifier. The proposed model incorporates a lever mechanism into the Sarrus linkage structure. It allows the target plate to move along one axis with amplified displacement in a parallel manner. The amplifier was assembled after machining the components using a computer numerical control machine. A flexible hinge was incorporated into the amplifier design for simplified fabrication and reduced friction in the actuation mechanism. Castigliano’s theorem is used to build a mathematical model of the proposed mechanical amplifier, and the performance was validated through finite element analysis and prototype fabrication. We achieved the amplification ratio of ×8.44, resulting in the axial displacement up to 86 µm. The demonstrated amplifier is expected to apply to compact microsurgical robots or biomedical imaging apparatus requiring coaxial displacement amplification in confined spaces.

关键词： Fabrication Couplings Technological innovation Resonant frequency Prototypes Predictive models Mathematical models

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Mimicking Muscle Relaxation Through 3D Print Materials & Magnetic Systems 19

Mimicking Muscle Relaxation Through 3D Print Materials & Mag...

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19th IEEE International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, HONET 2022

作者： Roberts, Santana Voicu, Razvan Cristian Moghadam, Amir Ali Amiri Chang, Yusun Kennesaw State University Department of Robotics & Mechatronics Engineering AtlantaGA United States

ISBN: (纸本)9781665461979

The purpose of this study is to examine the inter-action of a magnetic system with that of an elastic 3D print design to replicate a muscle in contraction expanding back to its original relaxed state. An electromagnetic system uses a design made of soft material to maintain a compression length when an electric current is applied. When the current is removed, the system expands back to its original length due to the geometry and elastic properties of the material. Preliminary experiments on differing magnets help establish a separation distance between the magnets within the design. Mathematical models then further detailed the design using the cross-sectional area, length, and angle between supports to estimate deflection. This design was then tested using nonlinear simulations to displace the component at a distance equivalent to the estimated distance given from the calculations. The reaction force was then compared to the force in the calculations to estimate the percent difference. Once decent results were obtained, a model was 3D printed and tested to verify the findings. The secondary goals of this endeavor are to utilize common materials, reduce the complexity of production, and limit the number of components within the system to reduce failure points along with an estimated lower cost for the overall apparatus. © 2022 IEEE.

关键词： Muscle

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Spectral and Morphological Classification of Celestial Objects using Physics Informed Machine Learning

Spectral and Morphological Classification of Celestial Objec...

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robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE International Conference on

作者： Md Fairuz Siddiquee Md Mehedi Hasan Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

The classification of celestial objects has traditionally relied on spectral analysis, which involves identifying unique patterns in the light emitted or absorbed by the objects. In this study, we present a novel approach to categorizing celestial objects based on their spectral properties and morphological structures, leveraging physics-informed machine learning (PIML) techniques to enhance the classification accuracy and efficiency. In the spectral analysis, using Sloan Digital Sky Survey (SDSS) dataset, we classified stars, galaxies, and quasars, focusing on redshift and near-infrared values with i and z filters. Our analysis employed three machine learning models (K-Neighbors, SVM, Random Forest), and the performances were verified to be resilient applying hyperparameter tuning and cross-validation. The best-performing model, Random Forest Classifier, achieved 98% accuracy. In the morphological analysis, the YOLO v5 model was utilized on a customized dataset of astronomical images to further classify the Galaxy Class into five distinct categories and the YOLOv5 model detected those with an average precision of 92.6% and recall of 68.9%. In addition, the interpretability of the model is enhanced by the incorporation of physical knowledge, allowing astronomers to gain deeper insights into the underlying processes. Integrating physical knowledge into these ML models enhances interpretability, facilitating automated real-time analysis of big astronomical data and advancing our understanding of the cosmos.

关键词： YOLO Surveys Support vector machines Analytical models Accuracy Stars Spectral analysis Random forests Tuning Robots

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Development of an Accelerometer-based Data Acquisition System for Hand Gesture Recognition

Development of an Accelerometer-based Data Acquisition Syste...

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robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE International Conference on

作者： Atique Tajwar Md. Ettashamul Haque Md Mehedi Hasan Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

This study presents a novel approach to Human-Robot Interaction (HRI) by employing gesture recognition to control robotic systems, such as bionic arms or manipulators, through accelerometer-based data collection. Using a low-cost glove equipped with an ESP32 microcontroller and accelerometer, data was gathered from 22 participants performing five distinct gestures: a clockwise circle, square, triangle, double tap, and the letter S. After filtering, these gestures were translated into features that trained a small neural network model designed for low-storage microcontrollers like the ESP32. With a performance accuracy of 90.4%, the neural network outperformed existing ensemble learning models, demonstrating its suitability for real-time gesture recognition tasks in embedded robotic systems. This highly efficient model enables seamless and intuitive control of robotic devices by facilitating responsive and natural HRI through gesture recognition. Designed specifically for compact and resource-limited environments, the model addresses the challenges associated with limited computational resources and restricted hardware capabilities. It provides a robust and scalable solution that can be easily integrated into various robotics applications, where gesture-based control is advantageous.

关键词： Microcontrollers Computational modeling Neural networks Human-robot interaction Low-pass filters Gesture recognition Data collection Manipulators Feature extraction Data models

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Experimental and numerical study of deposition mechanisms for cold spray additive manufacturing process

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Chinese Journal of Aeronautics 2022年第2期35卷 276-290页

作者： Tianyu YU Mingjun CHEN Zhuoru WU State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Department of Mechanical Engineering Iowa State UniversityAmes 50011USA

Cold spray is an attractive and rapidly developing process for additive manufacturing with high efficiency and precision,repairing and coating,especially in aircraft and aerospace *** spray additive manufacturing deposits micro-particles with large plastic deformation below their melting point,eliminating heat effect zone which could deteriorate the quality of repairing *** particle deposition in cold spray is a complex process which involves high strain rate,high contact pressure and high *** we develop,utilize and validate a thermomechanical model to provide a definitive way to predict deposition mechanics and surface deformation evolution for particle deposition process in cold spray additive *** a single particle and dual particles models were developed to investigate the contact interaction between particle/substrate and particle/*** combinations of particle/substrate materials(Cu/Cu,Al/Al,steel/steel,and nickel/nickel)and process parameters were considered in this *** experimental study was conducted to validate simulation results,providing useful information for understanding the limitations and challenges associated with cold spray additive *** framework provides insights into improving the quality and precision of stress/strain formation,particle interactions and particle deposition in cold spray additive manufacturing process.

关键词： Additive manufacturing Cold spray Finite element analysis Residual stresses Surface morphology

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