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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Hatcher, Grayson Tello, Marco Kirkwood, Nathan Kihei, Billy Kennesaw State University Department of Electrical and Computer Engineering MariettaGA United States Kennesaw State University Department of Robotics and Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

In recent years, competition within Radio Frequency (RF) communication bands has led to an overlap between the U-NII-4 band used by low-cost consumer devices, and the Dedicated Short Communication Band (DSRC) used by the Georgia Department of Transportation. Past research confirmed that this interference leads to drastic reduction in Packet Reception Rate (PRR) for Roadside Units (RSUs) and On-Board Units (OBUs) throughout the country, resulting in a reduction of stability between different components of the Intelligent Transportation System (ITS). Our research team set a goal to prove that a low-cost and simple modification can be made to RSUs currently in use that could increase PRR, by developing and testing RF technologies, primarily attenuators available on the market currently. These solutions could be easily adapted to infrastructure systems, and only require they run in-line with the stock antennas already in use. We were able to accomplish this goal, as well as made findings that may be beneficial within the world of RF communications for RSU devices. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Towards Autonomous Robotic Grasping: 3D Point Cloud Analysis with Stereo Cameras in Unpredictable Environments

Towards Autonomous Robotic Grasping: 3D Point Cloud Analysis...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Patel, Shrey Kovarovics, Aiden More, Aaditya Chun, Carrington Fletcher, Montreal Tanveer, M. Hasan Chakravarty, Sumit Voicu, Razvan Kennesaw State University Dept. of Robotics & Mechatronics Engineering Marietta United States Kennesaw State University Dept. of Electrical and Computer Engineering Marietta United States

ISBN: (纸本)9798331504847

Current robotic arm systems typically depend on pretrained models or function within controlled environments using known positions or fiducial markers for object manipulation. Although these methods achieve high accuracy, they face limitations due to lengthy training processes and the need for carefully controlled setups. This research introduces a methodology that leverages stereo cameras to develop a generalized algorithm aimed at overcoming these constraints and enhancing robotic manipulation in uncontrolled environments. The proposed solution utilizes an Intel RealSense stereo camera in conjunction with YOLO segmentation models for initial object identification. It combines segmentation meshes with the camera's depth values to create a detailed three-dimensional point cloud of the target object's surface. This point cloud is analyzed through Random Sample Consensus (RANSAC) and geometry-based feature extraction techniques, which allow the algorithm to approximate the object's shape as a combination of fundamental 3D forms, such as cuboids, cylinders, and spheres. A grasp point is then estimated by identifying suitable surfaces for grasping with a two-pronged gripper, followed by calculating an optimal approach angle and position kinematics for the robotic arm. This innovative approach enables robotic arm systems to autonomously adapt to various objects in uncontrolled environments, significantly expanding their practical applications. For example, the methodology could support modular robotic systems designed for sample collection in extraterrestrial missions, where adaptability and autonomous operations are essential. Ultimately, this research aims to enhance the efficiency and capabilities of robotic systems in dynamic and unpredictable settings. © 2025 IEEE.

关键词： Robotic arms

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AI-Driven 3D Printing: Generative Gcode for Quality and Efficiency

AI-Driven 3D Printing: Generative Gcode for Quality and Effi...

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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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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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Vibration-Assisted Hysteresis Mitigation for Achieving High Compensation Efficiency

arXiv

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arXiv 2025年

作者： Park, Myeongbo An, Chunggil Park, Junhyun Kang, Jonghyun Hwang, Minho Department of Robotics and Mechatronics Engineering DGIST Daegu42988 Korea Republic of

Tendon-sheath mechanisms (TSMs) are widely used in minimally invasive surgical (MIS) applications, but their inherent hysteresis—caused by friction, backlash, and tendon elongation—leads to significant tracking errors. Conventional modeling and compensation methods struggle with these nonlinearities and require extensive parameter tuning. To address this, we propose a vibration-assisted hysteresis compensation approach, where controlled vibrational motion is applied along the tendon’s movement direction to mitigate friction and reduce dead zones. Experimental results demonstrate that the exerted vibration consistently reduces hysteresis across all tested frequencies, decreasing RMSE by up to 23.41% (from 2.2345 mm to 1.7113 mm) and improving correlation, leading to more accurate trajectory tracking. When combined with a Temporal Convolutional Network (TCN)-based compensation model, vibration further enhances performance, achieving an 85.2% reduction in MAE (from 1.334 mm to 0.1969 mm). Without vibration, the TCN-based approach still reduces MAE by 72.3% (from 1.334 mm to 0.370 mm) under the same parameter settings. These findings confirm that vibration effectively mitigates hysteresis, improving trajectory accuracy and enabling more efficient compensation models with fewer trainable parameters. This approach provides a scalable and practical solution for TSM-based robotic applications, particularly in MIS. Copyright © 2025, The Authors. All rights reserved.

关键词： Friction

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Kirigami-inspired continuum soft arm with embedded sensing for non-destructive inspection and sorting

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Science China Materials 2025年第2期68卷 552-560页

作者： Jinsui Xu Boyi Xu Honghao Yue Yifan Lu Zheping Wang Zongquan Deng Fei Yang State Key Laboratory of Robotics and System School of Mechatronics EngineeringHarbin Institute of TechnologyHarbin 150001China Wu Xianming School of Intelligent Engineering South China University of TechnologyGuangzhou 510641China

The sensing capabilities of a soft arm are ofparamount importance to its overall performance as they allow precise control of the soft arm and enhance its interactionwith the surrounding environment. However, the actuationand sensing of a soft arm are not typically integrated into amonolithic structure, which would impede the arm’s movement and restrict its performance and application scope. Toaddress this limitation, this study proposes an innovativemethod for the integrated design of actuator structures andsensing. The proposed method combines the art of kirigamiwith soft robotics technology. In the proposed method, sensorsare embedded in the form of kirigami structures into actuatorsusing laser cutting technology, achieving seamless integrationwith a soft arm. Compared to the traditional amanogawakirigami and fractal-cut kirigami structures, the proposedmiddle-cut kirigami (MCK) structure does not buckle duringstretching and exhibits superior tensile performance. Based onthe MCK structure, an advanced interdigitated capacitivesensor with a high degree of linearity, which can significantlyoutperform traditional kirigami sensors, is developed. Theexperimental results validate the effectiveness of the proposedsoft arm design in actual logistics sorting tasks, demonstratingthat it is capable of accurately sorting objects based on sensorsignals. In addition, the results indicate that the developedcontinuum soft arm and its embedded kirigami sensors havegreat potential in the field of logistics automation *** work provides a promising solution for high-precisionclosed-loop feedback control and environmental interaction ofsoft arms.

关键词： kirigami embedded sensing continuous soft arm non-destructive inspection

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Humanoid robotics Powered by Vision-Based LLM Zero Shot Interactions

Humanoid Robotics Powered by Vision-Based LLM Zero Shot Inte...

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IEEE Southeastcon

作者： Gabriel David Smith Kwasi Debrah-Pinamang Razvan Cristian Voicu Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta Georgia USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Advancements in artificial intelligence (AI) have transformed robotics by enabling systems to autonomously execute complex tasks with minimal human involvement. Traditional methods, however, often depend on costly hardware, continuous monitoring, and intricate software integration, which constrain scalability and widespread implementation. This research presents an innovative approach that combines vision-based large language models (LLMs) with zero-shot prompting to autonomously program robotic systems, including a humanoid robot equipped with dual manipulators. The proposed system harnesses contextual image data to efficiently generate task-specific code, eliminating the need for iterative corrections. Training is conducted through OpenAI's Assistant feature, utilizing documents predominantly comprising images, while continuous operation is facilitated by a self-looping mechanism. Experimental results highlight the system's capability to perform manipulator tasks with notable accuracy, paving the way for scalable, adaptive, and dynamic automation. This study addresses both practical and theoretical challenges in automation, providing a cost-effective framework for next-generation robotic systems.

关键词： Training Accuracy Automation Large language models Scalability Humanoid robots Software Stereo vision Sensors Manipulator dynamics

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Modeling and simulation of soft bio-mimetic fingers with a novel soft thumb design for bionic hand applications using ANN

Biotechnology Notes

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Biotechnology Notes 2025年 6卷 164-176页

作者： Eman R.A. Soliman Ayman Nada Hiroyuki Ishii Ahmed M.R. Fathelbeb Mechatronics and Robotics Engineering Egypt Japan University of Science and Technology Alexandria Egypt School of Creative Science and Engineering Waseda University Tokyo Japan

The paper presents a novel design for a soft bio-mimetic finger and soft thumb structure for bionic hand applications. It introduces an anthropomorphic pneumatic flexible finger system using a PneuNets framework to enhance flexibility and maneuverability. The research investigates the influence of geometric variations (wall thickness, chamber number, and spacing) on finger deformation, demonstrating that reduced wall thickness and augmented chambers substantially improve flexibility. A key innovation is the soft thumb design that accurately replicates the complex movements of the Carpometacarpal (CMC) joint, enabling natural opposition and dexterity. Eight models were developed for four fingers and two models for the thumb. Simulation results indicate that models with thinner walls (2 mm) achieve bending angles exceeding 80° at 120 KPa, whereas 3 mm models remain below 50°. Moreover, increasing the number of chambers enhances deformation, with each added chamber contributing approximately 41 % more flexibility. For the thumb models, we successfully mapped the motion ranges and accurately mimicked the base joint, enabling natural opposition and dexterity. Furthermore, the paper also integrates Artificial Neural Networks (ANNs) to model forward kinematics, improving the estimation of bending angles and end-tip positions, which enhances the overall adaptability and control of the system.

关键词： Bionic hands Soft robotics PneuNets Soft thumb Neural network Artificial neural network (ANN) Soft robotics finger Anthropomorphic

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ChildACT: Child Action Recognition Dataset in RGB Data 25

ChildACT: Child Action Recognition Dataset in RGB Data

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Anara Sandygulova Andrey Yershov Aida Zhanatkyzy Zhansaule Telisheva Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Human action recognition is concerned with analysing, understanding and synthesizing kinematic and kinetic data. Data-driven computer vision solutions have advanced the progress in the field of action recognition while it would not be possible without large and publicly available datasets. While there are numerous datasets available for adult action recognition, child-centered action recognition efforts are still scarce. This paper presents a dataset that consists of video recordings of 200 children of different age and gender groups performing seven actions (boxing, waving, clapping, running, jogging, walking towards the camera, and walking from side to side). Two camera perspectives are provided, with a top view and a frontal view in RGB format. The dataset provides an opportunity for kinematic analysis of children motion while it also aims to bridge the performance gap between activity recognition systems for adults and children.

关键词： Training Legged locomotion Transfer learning Human-robot interaction Estimation Kinematics Cameras Human activity recognition Video recording Testing

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Vision-Based Motion Control for an 8-DOF Robotic Mobile Manipulator

Vision-Based Motion Control for an 8-DOF Robotic Mobile Mani...

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Annual IEEE Systems Conference

作者： Yunze Li Haoxiang Lang Ying Wang Department of Automotive and Mechatronics Engineering Ontario Tech University Oshawa ON Canada Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta GA USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

This paper introduces a visual servoing control strategy designed for a vision-based robotic mobile manipulation system. The system continuously updates the motion of both the mobile base and its integrated manipulator by utilizing visual feedback from a stereo camera. A detailed description of the system components is provided, including the mobile robot, the robotic manipulator, and the stereo vision system, along with the design of a visual servoing controller grounded in Lyapunov's stability theory. The controller operates by adjusting the movements of the mobile base and manipulator to minimize the disparity between the current and desired pose, ultimately guiding the system to achieve the target position and orientation. To validate the kinematic model and assess the performance of the proposed control strategy, experiments were conducted using the Husky mobile robot in conjunction with a 6-DOF UR5 robotic manipulator. The results from these experiments substantiate the effectiveness and robustness of both the robot model and the implemented control law. These findings confirm the potential of the proposed system in achieving reliable and precise mobile manipulation tasks.

关键词： Visualization Computational modeling Kinematics Transforms Manipulators Cameras Visual servoing Mathematical models 6-DOF Mobile robots

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