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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jaeyeong Keum Jaemin Kim Changgi Lee Seunghyun Lim Insung Ju Dongwon Yun Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu South Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this study, a throwing robot equipped with an shock absorbing structure, utilizing paired-Cross Flexural Hinge (p-CFH) and an airbag, was fabricated and validated to assess the effectiveness of its impact absorption mechanism. This robot was developed in anticipation of situations where direct human intervention for life rescue would be challenging. Throwing robots can be broadly categorized into ball type, wheel type, and hybrid type. The hybrid type combines the advantages of both: the ease of throwing from ball type, due to its low air resistance coefficient, and the versatile mobility of the wheel type in diverse environments. However, hybrid type throwing robots are more vulnerable to external impacts due to the complexity of their internal structure, resulting in a lower maximum drop height compared to wheel type *** address these challenges, this research proposes a the Throwbot that combines the easy throwing capability of ball type with the obstacle overcoming ability of the wheel type, while also addressing the low free fall height drawback inherent in hybrid types. To achieve this, we developed a Throwbot with a ball to wheel transform structure, p-CFH mechanism, and airbag based impact absorption system. Additionally, materials were selected based on simulation results to refine the Throw-bot. The performance of the proposed robot was evaluated through various assessments, including free fall experiments and obstacle overcoming tests. Through this research, the proposed Throwbot effectively addresses the shortcomings of existing throwing robots, establishing a novel approach to throwing robot design.

关键词： Absorption Electric shock Simulation Wheels Transforms Reconnaissance Mobile robots Robots Intelligent robots Immune system

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Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

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Computer Systems Science & engineering 2023年第2期44卷 961-977页

作者： E.Dhiravidachelvi M.Suresh Kumar L.D.Vijay Anand D.Pritima Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Electronics and Communication Engineering Mohamed Sathak A.J.College of EngineeringChennai603103India Department of Information Technology Sri Sairam Engineering CollegeChennai602109India Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatore641114India Department of Mechatronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artiﬁcial Intelligence(AI)*** techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and *** wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor ***,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,***-developed Artiﬁcial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR *** this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare *** proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare *** addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the ***-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of ***,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN ***,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different *** experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

关键词： Artiﬁcial intelligence human activity recognition deep learning deep belief network hyperparameter tuning healthcare

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Optimization of Polymeric Microneedle Electrode for High-Quality EMG recording

Optimization of Polymeric Microneedle Electrode for High-Qua...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jinwoong Jeong Yongwoo Kim Sanghoon Lee Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Republic of Korea

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper proposes the optimization of polyimide microneedle electrode using a 3D print double molding fabrication. Various shapes (circle, triangle, square, star, and hexagon) of microneedles are investigated by changing printing angles and double molding process. Aspect ratio, insertion force, and break point of the fabricated microneedles are investigated to maximize penetration capacity. Also, muscle signals were obtained using optimized microneedle electrodes to distinguish hand gesture.

关键词： Electrodes Fabrication Shape Force Stars Polyimides Muscles Three-dimensional printing Recording Optimization

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GripFlexer: Development of hybrid gripper with a novel shape that can perform in narrow spaces

GripFlexer: Development of hybrid gripper with a novel shape...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Donghyun Kim Sunghyun Choi Bongsub Song Jinhyeok Song Jingon Yoon Dongwon Yun Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu South Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, the role of robots across industries has become increasingly diverse, and they are now required to perform complex missions beyond simple repetitive tasks. However, robots used in confined spaces that humans cannot reach or in disaster field missions have challenges in performing various tasks due to their small size. In this study, we developed a compact hybrid gripper that fuses a multi-finger gripper and a jamming gripper to perform various tasks in a confined environment. Such a hybrid gripper can have both the strengths of a multi finger gripper that can perform various tasks and a jamming gripper that can effectively handle irregular small objects. In this study, we developed a hybrid gripper "GripFlexer" based on theoretical analysis and confirmed its performance through experiments by taking the task of turning a circular doorknob, which is one of the most difficult tasks in disaster sites, as the final target task. We also confirmed that the two grippers of GripFlexer can interact by showing performance improvement effects when two grippers are operated simultaneously.

关键词： Torque Service robots Shape Disasters Space missions Force Production Turning Grippers Jamming

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Design and Analysis of a Novel Hybrid Grasping Mode Microgripper for High Precision Tasks 9

Design and Analysis of a Novel Hybrid Grasping Mode Microgri...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and Computer engineering, WIECON-ECE 2023

作者： Das, Tilok Kumar Shahrear, Rifat Das, Arpita Shirinzadeh, Bijan Dept. of Mechanical Engineering Chattogram Bangladesh Temple University Dept. of Electrical and Computer Engineering PhiladelphiaPA United States Monash University Robotics and Mechatronics Research Laboratory Dept. of Mechanical and Aerospace Engineering MelbourneVIC Australia

ISBN: (纸本)9798350319651

This paper represents a novel piezoelectric actuated flexure-based compliant microgripper which can perform both parallel and angular grasping regardless of the geometric surface of the objects. Two compliant bridge-type mechanisms along with two symmetrical microgripper jaws at the end of the two side arms are accommodated to construct the microgripper. The two side arms of the microgripper act as lever-type amplification mechanisms. Simultaneous actuation of two bridge-type mechanisms is needed to perform linearized parallel grasping. The symmetric design is adopted to maintain better stability. The Finite Element Analysis (FEA) has been performed to evaluate the performance of the microgripper. FEA shows that each jaw of the microgripper can achieve a displacement of 141.72 μm resulting in a total displacement of 283.44 μm with an amplification ratio of 10.7 and 0.715% parasitic motion in y-direction of the X -axis displacement during parallel grasping. Also, each jaw can achieve a displacement of 283.88 μm which results in a total displacement of 567.76 μm with an amplification ratio of 21.5 and 5.17 mrad angular rotation about the Z-axis during angular grasping. The FEA suggests that the microgripper is capable of both parallel and angular grasping/releasing tasks. © 2023 IEEE.

关键词： Piezoelectric actuators

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

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

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

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

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

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.

关键词： Vibrations Visualization Accuracy Image recognition Tactile sensors Assistive technologies Motors Real-time systems Convolutional neural networks Wearable devices

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Vision Based Leader-Follower Control of Wheeled Mobile Robots using Reinforcement Learning and Deep Learning

Vision Based Leader-Follower Control of Wheeled Mobile Robot...

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International Symposium on System and Software Reliability (ISSSR)

作者： Kayleb Garmon Ying Wang Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta GA USA

Vision-based control of mobile robots often involves complex calculations to derive a control law. The reinforcement learning algorithm (Q-learning) offers a machine learning method to extrapolate a control law from an environment given discretized actions, without the need of complex calculations. In this paper, a vision-based controller is created using Q-Learning to enable tracking in a leader-follower configuration of two nonholonomic autonomous mobile robots. The follower robot gathers its desired trajectory values by using a deep learning SSD model to identify a distinguishing visual feature on the leader robot and uses a lidar to determine the distance between two robots. These parameters are utilized to select an optimal action of the follower robot through reinforcement learning. The emulated results in a ROS Gazebo environment show this method to be effective in enabling a wheeled mobile robot to follow another, while simultaneously avoiding obstacles.

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Exploring Neural Networks for Forward Kinematics of the Robotic Arm with Different Length Configurations: A Comparative Analysis 2

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

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

作者： Bouzid, Rania Narayan, Jyotindra Gritli, Hassène Higher Institute of Information and Communication Technologies Tunis Borj Cedria1164 Tunisia 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

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. © 2024 IEEE.

关键词： Robotic arms

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AgileVision Academy: Indoor Drone Navigation and Obstacle Avoidance 14

AgileVision Academy: Indoor Drone Navigation and Obstacle Av...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Jiang, Haolong Gu, Lingdan Wang, Jiayu Lan, Xiao Liu, Qinyao Wang, Fanxin Xi'an Jiaotong Liverpool University University of Liverpool School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou China Xi'an Jiaotong Liverpool University School of Robotics Department of Intelligence Robotics Engineering With Contemporary Entrepreneurialism Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Information and Computer Science Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Mechatronics and Robotics Suzhou China

ISBN: (纸本)9798350356670

Navigating cluttered indoor environments presents a significant challenge for aerial robots, requiring agility, speed, and a high level of reliability to avoid collisions. This project aims to address this challenge by developing advanced algorithms for lightweight drones, enabling them to reach target location and carrying out delivery efficiently and safely. Utilizing camera data to map and avoid obstacles, we create a system that not only maps and perceives the environment but also navigates its path to the destination. Included in the educational content, this project demonstrates the effectiveness of the technology through practical applications and real-world testing, helping the next generation of engineers accumulate technical experience and broaden their understanding of drone navigation algorithms and applications in system integration. It has a milestone impact on education and development of the drone field. © 2024 IEEE.

关键词： Drones

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Analytical and Deep Neural Network Based Hybrid Modelling of Single-Segment Continuum Robot

Analytical and Deep Neural Network Based Hybrid Modelling of...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Bhattacherjee, Saptak Chattopadhyay, Sudipta Sengupta, Aparajita Bhaumik, Subhasis School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Howrah Shibpur India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Howrah Shibpur India

ISBN: (纸本)9789811670107

Hyper-redundancy and ability to propagate through complex curvilinear pathways have made the continuum robots immensely popular in different fields of application. Precise control of continuum robot position relies on the accurate knowledge of the manipulator kinematics. However, the inverse kinematic model of continuum robots based on analytical modelling fails to include the unmodeled dynamics and effect of disturbances due to gravity and friction resulting in low accuracy in modelling and control. On the other hand, purely data driven continuum models require huge amount of data and time for training and suffer from high inaccuracy in modelling as well. Hybrid modelling in this scenario has been perceived to achieve better accuracy by combining the advantages of these two modelling approaches. Therefore, in the present work, we focus on the inverse kinematics of the single-segment continuum robot by using the analytical and deep neural network based hybrid modelling approach. Experimental results indicate that the proposed method increases the modelling accuracy and also at the same time it reduces the network training time significantly. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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