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SICE International Symposium on Control Systems (ISCS)

作者： Shawndy Michael Lee Jer Luen Chien Mario Josué Pérez Cruz Shaohui Foong Photchara Ratsamee Engineering Product Development Pillar Singapore University Technology and Design Singapore Department of Robotics and Systems Design Osaka Institute of Technology Osaka Japan

This paper presents an inverted perching mechanism designed for an unmanned aerial vehicle (UAV) intended for fluid spraying applications in the construction industry. The perching mechanism incorporates rotary and prismatic joints, enabling the UAV to change its body orientation while in a perched state. To ensure the effective operation of the inverted perching approach, several challenges including impact absorption, thrust control, and UAV body rotation need to be addressed. To tackle these challenges, the paper proposes the utilization of a Fuzzy Logic Control (FLC) thrust controller and an optimization-based UAV positioning strategy. The combined operation of these control strategies with the perching mechanism improves the UAV’s ceiling approach performance. Simulation and experimentation results demonstrate a relative increase in ±5° body rotation capability and a notable 17.65% improvement in impact absorption.

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Automatic Cataract and Cholesterol Detection System Based on Recurrent Neural Network (RNN): A Comparison with Convolutional Neural Network (CNN) and Siamese Neural Network (SNN) 1

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19th International Conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Lie, Zener S. Widodo, Jatmiko A. F. Hanesi, Isabel K. P. Moningka, Matthew R. N. Mariana, Y. Astuti, Winda Akmeliawati, Rini Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Product Design program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Robotics and Automation Research Group School of Electrical and Mechanical Engineering The University of Adelaide North Terrace Campus Adelaide Australia

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

In Indonesia, the majority of blindness cases, approximately 81%, are attributed to cataract. Additionally, 35% of the population has higher cholesterol levels than the standard normal range. The lack of specialized medical equipment, such as the slit-lamp (Ruparelia in Dalhousie Med J 49(1), [1]) bio-microscope, and trained professionals, especially in rural areas, make it difficult to detect these conditions through eye examinations. To address this issue, a supervised machine learning method, the Recurrent Neural Network (RNN), has been utilized to create an iris scanner system that can identify cataract and high cholesterol in a patient. This system will classify eyes into three categories, i.e., cataract, high cholesterol, and normal iris. The computer simulations indicate that this technique is more accurate and requires less training time compared to the existing Convolutional Neural Network (ANN) and Siamese Neural Network (SNN)-based method, while achieving 100% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Development of a Flexible Magnetic Multi-Arm Robot for Ceiling Inspection

Development of a Flexible Magnetic Multi-Arm Robot for Ceili...

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SICE Annual Conference

作者： Jer Luen Chien Shawndy Michael Lee Mario Josue Perez Cruz Shaohui Foong Photchara Ratsamee Engineering Product Development (EPD) Pillar of Singapore University of Technology and Design (SUTD) Singapore Department of Robotics and Systems Design Osaka Institute of Technology Osaka Japan

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

Continuum robots have the capability to perform flexible locomotion for navigation through tortuous paths. In this paper, we propose a multi-arm robot with multiple flexible tendon-driven continuum robots with magnets to demonstrate how compliance can be used to navigate through difficult environments. Furthermore, we propose an extended mathematical model from the Cosserat rod model to solve for the inverse kinematics of the robot to use motor displacements as inputs for robot control. We show an initial prototype of our system, and demonstrate the proposed robot's movement with an obstacle avoidance and an u-shaped locomotion experiment.

关键词： Navigation Robot control Prototypes Inspection Motors Manipulators Mathematical models Continuum robots Magnets Robots

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of Computer and Informatics Engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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Development of Detection Kidney Function Stand Alone System and design based on Oral Ammonia Levels 7

Development of Detection Kidney Function Stand Alone System ...

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7th International Conference of Computer and Informatics Engineering, IC2IE 2024

作者： William Hansen, Natasha Angeline Mariana, Yosica Sitepu, Elioenai Astuti, Winda Siswanto, Melinda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Biotechnology Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

The development of detection kidney function system at the first prototype have problem with the dependence on the internet, which make not practical in the development of the system and the initial design was not ergonomic. In this work the development of the second step is done with the improvement of accuracy compared with other methods. In this work the new design of the embedded system is also applied and in term of algorithm, the system applied for woman patient. Three different types of machine learning techniques, namely K-nearest neighbors (KNN), Decision Tree and Random Forest are applied and compared. The AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 90% and 91% for training and testing accuracy, respectively. The design of the embedded system also renews with stylist, simple and practical. © 2024 IEEE.

关键词： Support vector machines

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A Machine Learning-Based Predictive Framework for Head Impact Injuries

SSRN

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SSRN 2024年

作者： Heng, Jing Yi Chai, Rifai Mouloodi, Saeed Tse, Kwong Ming Department of Mechanical and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Telecommunications Electrical Robotics and Biomedical Engineering Swinburne University of Technology Melbourne Australia Department of Mechanical Engineering University of Melbourne Melbourne Australia

Modern technologies have rapidly developed in recent years, particular in the field of artificial intelligence (AI). Nascent technique has been adapted by researchers in their respective research fields to leverage the research outcome. Several research fields have achieved significance breakthroughs by embracing AI to address challenges. Machine learning (ML), a prominent subset of AI, is a data-driven method that enables the discovery and establishment of intricate relationship within datasets. Training ML models require large amounts of high-quality data to effectively capture and comprehend the underlying data relationships. Once adequately trained, these AI models can be deployed in real-life applications without the need for extensive computational power or resources. This research paper introduces the utilization of artificial neural networks (ANNs) to predict intracranial pressures using kinematic linear accelerations as inputs only. The proposed model incorporates three kinematic inputs (ax, ay, az) and time as inputs to the ANN. The developed ANN model exhibits the ability to predict intracranial pressure values for five distinct brain regions, representing the front, rear, left, right and rear sections of the brain. Both feedforward and time series ANNs were trained using 80 samples with 177 time-steps data. The ANN model successfully predicted intracranial pressure from head kinematics, with a correlation coefficient (R = 0.98) and root mean square error (RMSE = 0.008MPa). These findings highlight the efficacy of ANNs in accurately predicting intracranial pressure based on linear kinematic measurements. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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ARIS 1.0: An Autonomous Multitasking Medical Service Robot for Hospital Environments

ARIS 1.0: An Autonomous Multitasking Medical Service Robot f...

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IEEE International Conference on robotics and Automation (ICRA)

作者： D. M. A. P. Dunuwila W. M. L. N. Gunawardhana M. D. W. H. Basnayake Y. W. R. Amarasinghe A. G. B. P. Jayasekara H. A. G. C. Premachandra H. Tamura U-Xuan Tan Department of Mechanical Engineering University of Moratuwa Sri Lanka Department of Electrical Engineering University of Moratuwa Sri Lanka Engineering Product Development Pillar Singapore University of Technology and Design Singapore Department of Environmental Robotics University of Miyazaki Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Introducing robotics in the healthcare sector revolutionizes medical services by providing advanced treatments, medication management, and robotic assistance while overcoming resource limitations. In the current healthcare domain, an intermediate robotic communication platform is essential for distributing equal medical services, facilitating remote consultations, and maintaining the integrity of medical education, especially in rural areas and during pandemics. This work introduces ARIS, a multitasking medical service robot designed for telemedicine aspects and to facilitate remote medical education activities such as ward rounds. The prototype called ARIS 1.0 was developed, including a three-wheeled omnidirectional mobile platform, a torso and a novel movable neck mechanism with a face. The prototype robot can generate an online summarized report using its integrated language interaction and IoT-based vital sign extraction modules. The ROS-based semi-autonomous navigation facilitates the robot to be an assistive agent, allowing it to either accompany doctors or visit patients individually. Ultimately, ARIS 1.0 serves telepresence and novel regional language capabilities, specifically Sinhala-based self-communication features. This enables inter-party communication among doctors, medical students, and patients. The functionalities of ARIS 1.0 were validated in an emulated indoor environment to evaluate their feasibility. The results indicate that ARIS 1.0 is feasible for providing remote medical services. Furthermore, the paper discusses several promising research directions related to the proposed concept.

关键词： Torso Hospitals Telemedicine Education Prototypes Feature extraction Multitasking

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Automatic Anti-Swing Gantry Crane Based on Gradient Descent Optimization Proportional Integrated Derivative (PID): A Comparison Simulation and Real Experiment

Automatic Anti-Swing Gantry Crane Based on Gradient Descent ...

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2023 International Conference on Electrical Engineering and Informatics, ICEEI 2023

作者： Qadri, Munzir Harjanto, Danang Karuna Miharja, Jonathan Astuti, Winda Mariana, Yosica Tan, Sofyan Solihin, Mahmud Iwan University of Muhammadiyah Jakarta Engineering Faculty Mechanical Engineering Department Jakarta10510 Indonesia Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Binus Aso School of Engineering Bina Nusantara University Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Ucsi University Departement of Mechanical & Mechatronics Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350381290

Transporting payload with precise swing angles and transfer times poses a challenging task in building construction, which is why gantry crane systems are extensively utilized. Various control strategies, including open-loop and closed-loop systems, have been proposed to tackle this issue. However, most of these controllers rely on a model and parameters specific to the crane system, making the process of modeling and parameter identification complex and time-consuming. To address this challenge, this work presents a practical and simulation control method for automatic gantry cranes, which is evaluated simulation and experimentally. The lab-scale Gantry crane is developed in this work, which consists of mechanical and electrical parts. Proportional Integral and Derivative (PID) and Proportional Derivative (PD) controllers which applied optimization method are implemented in the electrical part of the system. The PID and PD controller applied in controlled the position and swing angle, respectively. The Gradient Decent optimization method is the best optimization method in determining the parameter of the controller, since it has the best simulation performance in the output of the system. The experimental result is developed and compared between simulation and lab-scale Gantry crane hardware implementation part. The performance of experimental results is shown appropriately approaching the performance of the simulation result, with the approximately settling time. © 2023 IEEE.

关键词： Proportional control systems

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Simplifying Robot Grasping in Manufacturing with a Teaching Approach based on a Novel User Grasp Metric

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Procedia Computer Science 2024年 232卷 1961-1971页

作者： Matteo Pantano Vladislav Klass Qiaoyue Yang Akhil Sathuluri Daniel Regulin Lucas Janisch Markus Zimmermann Dongheui Lee Technology Department Siemens Aktiengesellschaft D-81739 Munich Germany Human-Centered Assistive Robotics Technical University of Munich D-80333 Munich Germany Laboratory for Product Development and Lightweight Design Technical University of Munich D-85748 Garching Germany Autonomous Systems Technische Universität Wien AT-1040 Vienna Austria Institute of Robotics and Mechatronics German Aerospace Center D-82234 Wessling Germany

The manufacturing industry is undergoing rapid evolution, necessitating flexible and adaptable robots. However, configuring such machines requires technical experts, which are hard to find, especially for small and medium enterprises. Therefore, the process needs to be simplified by allowing non-experts to configure robots. During such configuration, one key aspect is the definition of objects’ grasping poses. The literature proposes deep learning techniques to compute grasping poses automatically and facilitate the process. Nevertheless, practical implementation for inexperienced factory operators can be challenging, especially if task-specific knowledge and constraints should be considered. To overcome this barrier, we propose an approach that facilitates teaching such poses. Our method, employing a novel user grasp metric, combines the operator's initial grasp guess given by a 3D spatial device with a state-of-the-art deep learning algorithm, thus returning reliable grasping poses but simultaneously close to the operator's initial guess. We compare this approach against commercial grasping pose definition interfaces through a user test involving 28 participants and against state-of-the-art deep learning grasp estimators. The results demonstrate a significant improvement in system usability (+24%) and a reduced workload (-16%). Furthermore, our experiments reveal an increased grasp success rate when utilizing the user grasp metric, surpassing state-of-the-art deep learning grasping estimators.

关键词： operator empowerment spatial interaction robotic grasping usability small medium enterprises cobots

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Determination of the Characteristics of Gears of Robot-Like Systems by Analytical Description of their Structure

Determination of the Characteristics of Gears of Robot-Like ...

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

作者： Stefan Landler Raúl Molina Blanco Michael Otto Birgit Vogel-Heuser Markus Zimmermann Karsten Stahl Technical University of Munich Germany TUM School of Engineering and Design Department of Mechanical Engineering Munich Institute of Robotics and Machine Intelligence (MIRMI) Institute of Machine Elements Gear Research Center (FZG) Institute of Automation and Information Systems (AIS) Laboratory for Product Development and Lightweight Design (LPL)

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

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