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ASME 2022 International mechanical engineering Congress and Exposition, IMECE 2022

作者： Papendorp, Sky Iyun, Olukayode Schneider, Christian Tekes, Ayse Ashuri, Turaj Amiri Moghadam, Amir Ali Robotics and Mechatronics Department Kennesaw State University MariettaGA United States Mechanical Engineering Department Kennesaw State University MariettaGA United States

ISBN: (纸本)9780791886663

This paper reports on the design, fabrication, and evaluation of a novel 3D printed vacuum based soft sensor and its application as hand motion sensor. Soft sensors are an integral part of soft robotics as an emerging field of science that enables safe and easy human to machine interaction. The conventional sensors used to interface with humans in robotic systems are mechanically incompliant. Because of this mechanical compliance mismatch, these sensors cause unsafe interactions with humans. Recent advancements in 3D printing technology have allowed fabrication of complex geometries with soft polymers, perfect for more delicate and user-friendly sensors. While the existing soft pneumatic sensors work based on the positive pressure due to deformation, the proposed sensor in this work works based on vacuum due to deformation. In comparison with the similar positive pressure sensor, our vacuum-based sensor is much softer and has minimum interference with hand motion. Finite element analysis (FEA) is used to analyze the design. Also, a kinematic model of the hand is developed using Matlab to visualize the hand motion based on the sensor data. This could show the potential application of the soft hand motion sensor in virtual reality and telehealth. Copyright © 2022 by ASME.

关键词： Finite element method

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Robot Path Planning via Neural-Network-Driven Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第3期3卷 451-460页

作者： Wang, Jiankun Liu, Jianbang Chen, Weinan Chi, Wenzheng Meng, Max Q.-H. Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen518172 China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong999077 Hong Kong

This article presents a novel heuristic method named neural-network-driven prediction (NEED) for robot path planning problems. Different from classical heuristic methods, NEED is not designed for some specific environments, which can be applied to various environments. NEED has an encoder-decoder structure, which consists of three modules: convolutional neural network backbone, spatial pooling module, and decoder module. By learning from a number of successful path planning cases, NEED can learn to analyze the environment structure and predict the promising search region for the new path planning problem. This predicted region serves as a heuristic to guide the search direction of path planning algorithms. A series of simulation experiments are conducted to demonstrate that NEED significantly improves the algorithm performance on solving new path planning problems. Meanwhile, NEED can also be applied to highly dynamic environments since it can output the prediction results at a speed of over 100 Hz on a laptop. © 2020 IEEE.

关键词： Intelligent robots

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Preliminary Characterization of a Plastic Piezoelectric Motor Stator Using High-Speed Digital Holographic Interferometry 1

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SEM Annual Conference and Exposition on Experimental and Applied Mechanics, 2020

作者： Zhao, Z. Carvalho, P.A. Tang, H. Pooladvand, K. Gandomi, K.Y. Nycz, C.J. Furlong, C. Fischer, G.S. Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA United States Mechanical Engineering Department Worcester Polytechnic Institute WorcesterMA United States

ISBN: (数字)9783030597733

ISBN: (纸本)9783030597726

Precise surgical procedures such as deep brain tumor ablation may benefit from intra-operative image guidance using magnetic resonance imaging (MRI). However, the MRI’s strong magnetic fields and constrained space pose the need for robotic devices to assist the surgeon. Piezoelectric motors are often used to actuate these robots. The piezoelectric resonant motor (PRM) is a class of such motors that consist of a bonded piezoelectric ring stator and a frictionally coupled rotor. Steady-state excitation at certain frequencies leads to specific mode shapes on the stator with surface waves having both in-plane and out-of-plane displacement components that cause the coupled rotor to spin. High-speed digital holography (HDH) can be used to measure time variant displacements with nanometer and microsecond resolution. We present initial measurements acquired at 67 k frame per second (fps) of two custom-designed plastic stator components operating at drive frequencies of 6.788 kHz and 6.980 kHz, respectively. Circumferential motion of the traveling surface waves with out-of-plane peak-to-peak displacement of approximately 100 nm peak-to-peak amplitude and a settling time of 2.99 ms was observed. Results demonstrate that plastic stators may be a promising alternative to metallic stators for use in the MRI environment. © 2021, The Society for Experimental Mechanics, Inc.

关键词： Stators

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A Soft e-Textile Sensor for Enhanced Deep Learning-based Shape Sensing of Soft Continuum Robots

A Soft e-Textile Sensor for Enhanced Deep Learning-based Sha...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Eric Vincent Galeta Ayman A. Nada Sabah M. Ahmed Victor Parque Haitham El-Hussieny Department of Mechatronics and Robotics Egypt-Japan University of Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering Assiut University Egypt Department of Modern Mechanical Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics, where the limitations of traditional rigid sensors become evident. Encoders, piezoresistive, and potentiometer sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. To overcome these hurdles, our study presents a new approach to shape sensing in soft continuum robots through the use of soft e-textile resistive sensors. This sensor, designed to flawlessly integrate with the robot’s structure, utilizes a resistive material that adjusts its resistance in response to the robot’s movements and deformations. This adjustment facilitates the capture of multidimensional force measurements across the soft sensor layers. A deep Convolutional Neural Network (CNN) is employed to decode the sensor signals, enabling precise estimation of the robot’s shape configuration based on the detailed data from the e-textile sensor. Our research investigates the efficacy of this e-textile sensor in determining the curvature parameters of soft continuum robots. The findings are encouraging, showing that the soft e-textile sensor not only matches but potentially exceeds the capabilities of traditional rigid sensors in terms of shape sensing and estimation. This advancement significantly boosts the safety and efficiency of robotic navigation systems.

关键词： Training Shape Navigation Estimation Sensor phenomena and characterization Robot sensing systems Sensors Safety Convolutional neural networks Continuum robots

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Solution for Agri Disaster Using Climate Changes Adaptation by Ml 6

Solution for Agri Disaster Using Climate Changes Adaptation ...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ganesh, L. Venkatesh, R. Prasanna Kumar, Kaushal Kumari, Khushboo Borkakoty, Sangeeta Raju, A. Rajalakshmi Engineering College Robotics and Automation Dept. Tamilnadu Chennai India SACS MAVMM Engineering College Department of Mechanical Engineering Tamilnadu Madurai India School of Life Sciences Department of Botany Mahatma Gandhi Central University Bihar India University of Science & Technology Department of Computer Science & Electronic Meghalaya India KLEF Vaddeswaram Green Field Department of Commerce Vijayawada India

ISBN: (纸本)9798350304480

This paper proposes using good sized getting to know fashions to recognize lesions of cotton leaves in the context of pictures of the reaping within the subject. Cotton is among the maximum economically essential plant harvests, as it's far grown all over the international. Its spread in tropical areas has made it the goal of a wide variety of farming irritations and illnesses, necessitating succesful plans. moreover, the consequences of commonplace disturbances and disorders are difficult to isolate inside the early stages, and the author may additionally battle to locate the suitable visible verification of a scientific difficulty. To help in resolving the difficulty, it contemporary inquiry proposes a solution that takes into consideration substantial training within the filtering of cotton leaves, making it possible to reveal the cotton yield's health and make higher judgments approximately its management. The studying models GoogleNet like Resnet50, which use convolutional cerebrum associations, performed an accuracy of 86.6 percentage and 89.2 percentage, respectively. assist vector machines, Closest k-pals, faux creativeness associations, and neuro-tender are some of the extraordinary as well as everyday tactics again for treatment of photographs. The convolutional cerebellum networks ended up being 25% extra unique, implying that this method can upload to a more strong and short investigation of the flora looking to fill inside the field. © 2023 IEEE.

关键词： Artificial Intelligence Convolutional Neural Networks Image Processing Precision Agriculture

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Advancement in Smart Cane Technology: Enhancing Mobility for the Visually Impaired Using ROS and LiDAR

Advancement in Smart Cane Technology: Enhancing Mobility for...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Saqib Zafar Hafiz Farhan Maqbool Naveed Ahmad Arham Ali Abdul Moeizz Faizan Ali Juri Taborri Stefano Rossi Department of Economics Engineering Society and Business Organization University of Tuscia Viterbo VT Italy Department of Mechanical Mechatronics and Manufacturing Engineering University of Engineering and Technology Lahore (Faisalabad Campus) Pakistan Human-Centred Robotics Lab National Center of Robotics and Automation Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Visual impairment affects the ability to navigate independently in an unknown environment due to reliance on human/dog assistance, potential miscommunication, and discomfort during mobility. To make the visually impaired (VI) individuals self-reliant and confident in their mobility the proposed system has been developed that contains a light detection and ranging (LiDAR) sensor for obstacle detection and two omni-wheels coupled with the DC gear encoder motors for precise movement in any direction without lifting the cane. Hector Simultaneous Localization and Mapping (SLAM), A *, and steering wheel algorithms have been implemented in the robot operating system (ROS) for real-time environment mapping, efficient path planning, and navigating in an unknown indoor static environment, respectively. The Raspberry Pi 3B+ and Arduino Mega 2560 are used in the master-slave configuration for decision-making on path planning and controlling the maneuverability of the system in real-time. The proposed system in the paper is successfully tested on one subject in two indoor environments. The results demonstrate that the system serves as an assistive mobile robot, helping the VI individual to navigate confidently in an unknown environment and reach the destination without assistance.

关键词： Simultaneous localization and mapping Laser radar Navigation Operating systems Decision making Visual impairment Wheels Real-time systems Path planning robotics and automation

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Knee osteoarthritis severity detection using deep inception transfer learning

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Computers in Biology and Medicine 2025年 186卷 109641-109641页

作者： Sohail, Muhammad Azad, Muhammad Muzammil Kim, Heung Soo Department of Mechanical Robotics and Energy Engineering Dongguk University-Seoul 30 Pildong-ro 1-gil Jung-gu Seoul04620 Korea Republic of

Osteoarthritis (OA) is a prevalent condition resulting in physical limitations. Early detection of OA is critical to effectively manage this condition. However, the diagnosis of early-stage arthritis remains challenging. The Kellgren and Lawrence (KL) grading system is a common method that is accepted worldwide, uses five grades to classify the severity of OA, and relies on the ability of the orthopedist to accurately interpret radiograph images. To improve the accuracy of radiograph image interpretation, artificial intelligence-assisted models have been developed that include shallow or deep learning approaches and multi-step techniques;however, their accuracy remains variable. This work proposes a transfer learning approach using an InceptionV3-based model fine-tuned on the Osteoarthritis Initiative dataset, and aims to enhance the identification of OA severity levels through dual-stage preprocessing and convolutional neural networks for feature extraction. The fine-tuned IV3 (FT−IV3) model outperformed the IV3 model with training, validation, and testing accuracies of (96.33, 93.82, and 92.25) %, compared to IV3 accuracies of (91.64, 82.04, and 86.20) %, respectively. Additionally, Cohen's Kappa value for the FT−IV3 model (90.69 %) exceeds that of the IV3 model (83.15 %), indicating a better diagnosis of OA severity. This improvement allows the FT−IV3 model to effectively classify moderate and severe-grade OA. © 2024 Elsevier Ltd

关键词： Joints (anatomy)

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Modeling and Simulation of Dynamics in Soft robotics: a Review of Numerical Approaches

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Current robotics Reports 2023年第1期5卷 1-13页

作者： Qin, Longhui Peng, Haijun Huang, Xiaonan Liu, Mingchao Huang, Weicheng School of Mechanical Engineering Southeast University Nanjing China Department of Engineering Mechanics Dalian University of Technology Dalian China Robotics Department University of Michigan Ann Arbor USA School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Republic of Singapore

In this review, we briefly summarize the numerical methods commonly used for the nonlinear dynamic analysis of soft robotic systems. The underlying mechanical principles as well as the geometrical treatment tailored for soft robots are introduced with particular emphasis on one-dimensional models. Additionally, the review encompasses three-dimensional frameworks, available simulation packages, and various types of interaction models, shedding light on the design, actuation, motion control, and internal and external forces of soft robots. Reduced-order models can offer high efficiency in characterizing nonlinear deformations, allowing convenient tailoring based on specific structural and material configurations. For pursuing high simulation accuracy and detailed mechanics, the finite element method proves to be a valuable tool through numerous off-the-shelf platforms. Furthermore, machine learning has emerged as a promising tool to effectively address the challenges within the mechanics community. A wide range of kinematic and dynamic numerical models is available for simulating the behaviors of soft robots, offering exceptional adaptability to different geometries and structures based on existing modeling theories and numerical solution algorithms. However, the trade-off between computational complexity and simulation accuracy remains a challenge in achieving fast, accurate, and robust control of soft robots in complex environments.

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Thermal Hall effect in insulating quantum materials

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The Innovation 2022年第5期3卷 3-4页

作者： Shucheng Guo Youming Xu Ran Cheng Jianshi Zhou Xi Chen Department of Electrical and Computer Engineering University of CaliforniaRiversideRiversideCA 92521USA Department of Physics and Astronomy University of CaliforniaRiversideRiversideCA 92521USA Materials Science and Engineering Program Department of Mechanical EngineeringUniversity of Texas at AustinAustinTX 78712USA

The emerging field of quantum materials involves an exciting new class of materials in which charge,spin,orbital,and lattice degrees of freedom are intertwined,exhibiting a plethora of exotic physical *** materials include,but are not limited to,superconductors,topological quantum matter,and systems with frustrated spins,which enable a wide range of potential applications in biomedicine,energy transport and conversion,quantum sensing,and quantum information processing。

关键词： properties. quantum topological

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Accelerated Informed RRT*: Fast and Asymptotically Path Planning Method Combined with RRT*-Connect and APF 16th

Accelerated Informed RRT*: Fast and Asymptotically Path Pla...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Tu, Zhixin Zhuang, Wenbing Leng, Yuquan Fu, Chenglong Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819965007

In recent years, path planning algorithms have played a crucial role in addressing complex navigation problems in various domains, including robotics, autonomous vehicles, and virtual simulations. This abstract introduces a improved path planning algorithm called Informed RRT*-connect based on APF, which combines the strengths of the fast bidirectional rapidly-exploring random tree (RRT-connect) algorithm and the informed RRT* algorithm. The proposed algorithm aims to efficiently find collision-free paths with less iterations and time while minimizing the path length. Unlike traditional RRT-based algorithms, Informed RRT*-connect based on Artificial Potential Fields (APF) incorporates a bidirectional connection and rewiring of a new sampling point to explore the search space. This enables the algorithm to connect both the start and goal nodes more effectively and quickly to find a initial solution, reducing the search time and provide a better initial heuristics sapling for the next optimal steps. Furthermore, Informed RRT*-connect introduces an informed sampling strategy that biases the sampling towards areas of the configuration space likely to yield better paths. This approach significantly reduces the exploration time to find a path and enhances the ability to discover optimal paths efficiently. To evaluate the effectiveness of the Informed RRT*-connect algorithm, we conducted the simulation experiments on two different experiment protocol. The results demonstrate that our approach outperforms existing state-of-the-art algorithms in terms of both planning efficiency and solution optimality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Motion planning

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