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

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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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Implicit Grasp Diffusion: Bridging the Gap between Dense Prediction and Sampling-based Grasping 8

Implicit Grasp Diffusion: Bridging the Gap between Dense Pre...

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8th Conference on Robot Learning, CoRL 2024

作者： Song, Pinhao Li, Pengteng Detry, Renaud KU Leuven Dept. Mechanical Engineering Research Unit Robotics Automation and Mechatronics Belgium KU Leuven Dept. Electrical Engineering Research Unit Processing Speech and Images Belgium AI Thrust Hong Kong

There are two dominant approaches in modern robot grasp planning: dense prediction and sampling-based methods. Dense prediction calculates viable grasps across the robot's view but is limited to predicting a fixed number of grasps per voxel. Sampling-based methods, on the other hand, encode multi-modal grasp distributions, allowing for different grasp approaches at a point. However, these methods rely on a global latent representation, which struggles to represent the entire field of view, resulting in coarse grasps. To address this, we introduce Implicit Grasp Diffusion (IGD), which combines the strengths of both methods by using implicit neural representations to extract detailed local features and sampling grasps from diffusion models conditioned on these features. Evaluations on clutter removal tasks in both simulated and real-world environments show that IGD delivers high accuracy, noise resilience, and multi-modal grasp pose capabilities. Our code is freely available at https://***/detry-lab/public/***. © 2024 Proceedings of Machine Learning Research.

关键词： Robot programming

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Improving Road Safety With Enhanced Vehicle Detection for Night-time Traffic in Mirpur 6

Improving Road Safety With Enhanced Vehicle Detection for Ni...

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

作者： Younis, Usama Riaz, Faisal Raza, Umar Atali, Gokhan Rabnawaz, Hamid HEC Control Automotive and Robotics Lab Islamabad Pakistan Manchester Metropolitan University Department of Engineering Manchester United Kingdom Mechatronics Engineering Sakarya University of Applied Sciences Sakarya54050 Turkey Selftech Pvt Ltd Pakistan

ISBN: (纸本)9798350355703

This paper presents a study on improving night-time vehicle detection in the Mirpur region through a modified neural network model. Given the challenges of low visibility at night, the research focused on optimizing a deep learning algorithm to enhance detection accuracy. A dataset of 10,000 images covering various vehicle types was collected and annotated to train and test the model. The study found a 7% increase in detection performance compared to the standard model, highlighting the potential for tailored algorithms in specific environmental conditions. The findings suggest significant implications for road safety and autonomous driving systems, particularly in regions with similar night-time driving challenges. The research contributes to the broader field of computer vision by showing the effectiveness of model optimization in real-world conditions. © 2024 IEEE.

关键词： Vehicle safety

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CO2 Laser Fabrication of a Passive continuous-flow T-shaped Polymethyl Methacrylate (PMMA) Micromixer

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Lasers in Manufacturing and Materials Processing 2023年第3期10卷 373-388页

作者： Okello, Job Lazarus El-Bab, Ahmed M. Fath Yoshino, Masahiko El-Hofy, Hassan A. Industrial and Manufacturing Engineering Department University of Science and Technology New Borg El-Arab City Egypt Mechatronics and Robotics Engineering Department University of Science and Technology New Borg El-Arab City Egypt Department of Mechanical Engineering Tokyo Institute of Technology Tokyo Japan On leave from Department of Production Engineering Faculty of Engineering Alexandria University Alexandria Egypt

Since the emergence of microfluidics, the demand for microfluidic devices is ever increasing. Polymethyl methacrylate (PMMA) is extensively used to manufacture microfluidic devices because it is cheap, disposable, and biocompatible. CO2 laser micromachining of PMMA is cheap, fast, and efficient. However, thermal defects affect the process. In this work, a passive continuous-flow T-shaped micromixer was fabricated using a low-power CO2 laser. Microchannels were fabricated on PMMA substrates coated with a 1-µm layer of 99.95% pure aluminium. The power and speed of the laser varied from 1.5 to 7.5 W and 5 to 17.5 mm/s respectively. The effects of the deposited energy on surface roughness, heat-affected zone, and volumetric removal rate were examined. The microchannels were characterized to get the suitable dimensions for the micromixer’s microchannel which was fabricated at 3.0 W and 17.5 mm/s. The micromixer was bonded using the microwave welding technique and tested for leakage using purple dye by varying the flow pressure at intervals of 100 kPa for 20 min. Up to 1.5 MPa, no leakage occurred. Beyond 1.5 MPa, any increase in pressure resulted in leakage from the inlet tubes. Coating PMMA substrates with 99.95% pure aluminium before fabrication reduces thermal defects and improves efficiency. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Carbon dioxide lasers

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Design and Development of a Humanoid Robot for Sign Language Interpretation

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SN Computer Science 2021年第3期2卷 220页

作者： Nihal, Ragib Amin Broti, Nawara Mahmood Deowan, Shamim Ahmed Rahman, Sejuti Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh

Purpose: With an indisputable complexity of communication for hearing and speaking impaired people, most sign language recognition systems utilize virtual reality or onscreen robots. This paper presents the design and development of a special and low-cost humanoid robot that can perform as a sign language interpreter. To the best of our knowledge, this is the first endeavor to fabricate a humanoid robot for Bangla sign language (BdSL) and Medical signs interpretation. Methods: Considering the plethora of design criteria and balancing between rigidity and flexibility 3D models of the robotics parts are designed and 3D printed ensuring cost efficiency. With the help of modern fabrication technology, the robot is developed and assembled with proper actuators and circuitry. An image dataset is built comprising 950 images for BdSL recognition and made publicly available. We utilized the Recurrent neural network (RNN) and Convolutional neural network (CNN) for deep learning model establishment and feature extraction from video and image data. Results: The developed humanoid robot has 43 Degrees of freedom (DoF) which includes two 15 DoF hands. It can imitate 16 BdSL alphabets in sign language, can capture a video or image input in real-time from the user, and recognize 10 medical signs and 38 alphabets of BdSL. The learning model for video-based medical sign recognition achieved 87.5% test accuracy. Image-based Bangla sign language recognition achieved an overall test accuracy of 98.19% in our dataset and 93.8% in another available dataset. Conclusion: Compared to the state-of-the-art robotic systems for sign language interpretation, our approach has achieved higher kinematic characteristics, remarkable results in sign recognition, and impressive competency in sign imitation;all at almost 10 times lower cost than the state-of-the-art systems. The results are evidence that our approach is efficient and suitable in helping hearing and speaking impaired people. Moreover, thi

关键词： Convolutional neural network Humanoid robot Recognition Recurrent neural network Sign language

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Exploring the Feasibility of Solar-Powered Autonomous Underwater Survey Vehicles for Extended Underwater Exploration

Exploring the Feasibility of Solar-Powered Autonomous Underw...

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IEEE Southeastcon

作者： Alan Bounyasith Ben Mueller Dongwoo Jin Uday Patel M. Hassan Tanveer Matthew Marshall Yusun Chang Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta GA USA

Autonomous Underwater Vehicles (AUVs) have emerged as vital tools in underwater ecological research. However, challenges such as limited operating range, energy constraints, and communication issues persist. This research investigates the practicality of autonomous survey vehicles by proposing a solution that integrates renewable energy sources, enhances communication capabilities during submersion, and facilitates long-term deployments. The study presents a solar-powered Autonomous Underwater Survey Vehicle (AUSV), comprising a submersible and a tethered solar buoy. The AUSV aims to operate autonomously along predefined paths, ensuring constant communication, extended operation cycles, and efficient data collection. The solar buoy provides GPS tracking, data transmission, and renewable energy generation, eliminating the need for periodic resurfacing. The submersible is equipped with thrusters, sensors, and data acquisition tools for oceanic measurements. The project's focus is on addressing limitations in traditional survey methods, providing an economical and efficient alternative for underwater exploration, with potential applications in ecological studies, water quality testing, and bathymetry. The presented proof-of-concept prototype lays the groundwork for future advancements in underwater infrastructure and scientific exploration. Keywords: Solar autonomous underwater vehicle, renewable energy, AUV, underwater mobile robots, oceanic survey, autonomous exploration.

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Model- and Data-Based Control of Self-Balancing Robots: Practical Educational Approach with LabVIEW and Arduino

arXiv

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arXiv 2024年

作者： Abdelgawad, Abdelrahman Shohdy, Tarek Nada, Ayman Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt

A two-wheeled self-balancing robot (TWSBR) is non-linear and unstable system. This study compares the performance of model-based and data-based control strategies for TWSBRs, with an explicit practical educational approach. Model-based control (MBC) algorithms such as Lead-Lag and PID control require a proficient dynamic modeling and mathematical manipulation to drive the linearized equations of motions and develop the appropriate controller. On the other side, data-based control (DBC) methods, like fuzzy control, provide a simpler and quicker approach to designing effective controllers without needing in-depth understanding of the system model. In this paper, the advantages and disadvantages of both MBC and DBC using a TWSBR are illustrated. All controllers were implemented and tested on the OSOYOO self-balancing kit, including an Arduino microcontroller, MPU-6050 sensor, and DC motors. The control law and the user interface are constructed using the LabVIEW-LINX toolkit. A real-time hardware-in-loop experiment validates the results, highlighting controllers that can be implemented on a cost-effective platform. © 2024, CC BY.

关键词： Controllers

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Gesture-Guided Exosuit Interface: Advancing Motion Tracking and Automation for Robotic Control

Gesture-Guided Exosuit Interface: Advancing Motion Tracking ...

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IEEE Southeastcon

作者： Jaykob Aksoy Jose Gonzalez Siddhant Ala Franccesca Tabertus Jose Chagoya Charles Koduru M. Hassan Tanveer Matthew Marshall Yusun Chang Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta GA USA

Exosuits, also known as exoskeletons, represent a burgeoning technology designed to augment human physical capabilities while alleviating bodily strain. This project introduces a novel approach to exosuit functionality, focusing on motion tracking through a fused deposition modeled apparatus with rotary hall effect sensors. The system precisely monitors shoulder and elbow joint movements, translating them into control signals for a two-wheeled robot. The innovative design aims to provide an affordable, hands-free solution for motion tracking, offering applications in the control and automation of various vehicles, machines, and equipment. The research addresses challenges such as sensor data fusion, aiming to seamlessly integrate sensor information for real-time control. As exosuits evolve, the emphasis lies in minimizing size, enhancing comfort, and expanding applications, potentially leading to inconspicuous, wearable devices with broad compatibility. The goal is to create user-friendly exosuit with a modular design, capable of controlling a variety of devices, with minimal setup and calibration. This project contributes to the ongoing development of exosuit technology, offering a promising avenue for future applications in fields ranging from virtual reality, medical and industrial domains.

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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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Data Collection, Heat Map Generation for Crack Detection Using Robotic Dog Fused with FLIR Sensor

Data Collection, Heat Map Generation for Crack Detection Usi...

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IEEE Southeastcon

作者： Jose Chagoya Shrey Patel Charles Koduru Aiden Kovarovics M. Hassan Tanveer Razvan C. Voicu Department of Robotics and Mechatronics Engineering Kennesaw State University Marietta GA USA

The imperative need to monitor critical infrastructure, encompassing power plant transformers and essential civil structures, is paramount for ensuring their sustained longevity and safety. A notable progression in this domain arises from the widespread availability of Forward-Looking Infrared (FLIR) cameras, made possible by reductions in manufacturing costs. These FLIR cameras, distinguished by their remarkable precision in temperature data collection and visualization, hold significant potential across diverse fields. This paper underscores the pioneering application of FLIR cameras in infrastructure monitoring, with a specific focus on power transformers and aging civil structures like bridges. In the realm of transformers, the FLIR camera exhibits the capability to delve into and record internal temperatures, facilitating the early identification of deviations from standard operational temperature ranges. Detection of such anomalies serves as a trigger for preemptive measures, thereby minimizing operational disruptions. Similarly, for structures such as bridges, routine FLIR analyses prove instrumental in discerning temperature variations that may indicate potential wear or fractures. This timely identification allows for proactive maintenance, ensuring structural integrity and averting potential hazards. To augment the safety and efficiency of data collection, this research advocates for the integration of FLIR cameras with mobile robots, exemplified by the Unitree GoAirl. This strategic amalgamation enables precise yet remote surveillance, mitigating risks to operators while maximizing the accuracy of infrastructure health assessments. The proposed approach not only advances the capabilities of FLIR technology but also her-alds a paradigm shift in the realm of infrastructure monitoring, promising enhanced reliability and safety standards.

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