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Neural Models and Algorithms for Sensorimotor Control of an Octopus Arm

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arXiv 2024年

作者： Wang, Tixian Halder, Udit Gribkova, Ekaterina Gillette, Rhanor Gazzola, Mattia Mehta, Prashant G. Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignIL61801 United States Coordinated Science Laboratory University of Illinois Urbana-ChampaignIL61801 United States Neuroscience Program University of Illinois Urbana-ChampaignIL61801 United States Department of Molecular and Integrative Physiology University of Illinois Urbana-ChampaignIL61801 United States National Center for Supercomputing Applications University of Illinois Urbana-ChampaignIL61801 United States Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-ChampaignIL61801 United States

In this article, a biophysically realistic model of a soft octopus arm with internal musculature is presented. The modeling is motivated by experimental observznations of sensorimotor control where an arm localizes and reaches a target. Major contributions of this article are: (i) development of models to capture the mechanical properties of arm musculature, the electrical properties of the arm peripheral nervous system (PNS), and the coupling of PNS with muscular contractions;(ii) modeling the arm sensory system, including chemosensing and proprioception;and (iii) algorithms for sensorimotor control, which include a novel feedback neural motor control law for mimicking target-oriented arm reaching motions, and a novel consensus algorithm for solving sensing problems such as locating a food source from local chemical sensory information (exogenous) and arm deformation information (endogenous). Several analytical results, including rest-state characterization and stability properties of the proposed sensing and motor control algorithms, are provided. Numerical simulations demonstrate the efficacy of our approach. Qualitative comparisons against observed arm rest shapes and target-oriented reaching motions are also reported. © 2024, CC BY-NC-ND.

关键词： Stability

high-entropy silicide:(Mo_(0.2)Nb_(0.2)Ta_(0.2)Ti_(0.2)W_(0.2))Si_(2)

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Journal of Materiomics 2019年第3期5卷 337-343页

作者： Joshua Gild Jeffrey Braun Kevin Kaufmann Eduardo Marin Tyler Harrington Patrick Hopkins Kenneth Vecchio Jian Luo Program of Materials Science and Engineering University of CaliforniaSan DiegoLa JollaCA92093-0418USA Department of Mechanical and Aerospace Engineering University of VirginiaCharlottesvilleVA22904USA Department of NanoEngineering University of CaliforniaSan DiegoLa JollaCA92093-0448USA

A high-entropy metal disilicide,(Mo_(0.2)Nb_(0.2)Ta_(0.2)Ti_(0.2)W_(0.2))Si_(2),has been successfully synthesized.X-ray diffraction(XRD),energy dispersive X-ray spectroscopy(EDX),and electron backscatter diffraction(EBSD)collectively show the formation of a single high-entropy silicide *** high-entropy(Mo_(0.2)Nb_(0.2)Ta_(0.2)Ti_(0.2)W_(0.2))Si_(2) possesses a hexagonal C40 crystal structure with ABC stacking sequence and a space group of *** discovery expands the known families of high-entropy materials from metals,oxides,borides,carbides,and nitrides to a silicide,for the first time to our knowledge,as well as demonstrating that a new,non-cubic,crystal structure(with lower symmetry)can be made into highentropy ***(Mo_(0.2)Nb_(0.2)Ta_(0.2)Ti_(0.2)W_(0.2))Si_(2) exhibits high nanohardness of 16.7±1.9 GPa and Vickers hardness of 11.6±0.5 ***,it has a low thermal conductivity of 6.9±1.1Wm^(-1) K^(-1),which is approximately one order of magnitude lower than that of the widely-used tetragonal MoSi_(2) and ~1/3 of those reported values for the hexagonal NbSi_(2) and TaSi_(2) with the same crystal structure.

关键词： High-entropy ceramics High-entropy silicide Thermal conductivity Hardness C40 crystal structure

Resolution-Enhanced MRI-Guided Navigation of Spinal Cellular Injection Robot

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arXiv 2020年

作者： Martinez, Daniel Enrique Meinhold, Waiman Oshinski, John Hu, Ai-Ping Ueda, Jun Robotics PhD Program Georgia Institute of Technology Department of Radiology Emory University Food Processing Technology Division Georgia Tech Research Institute George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology

This paper presents a method of navigating a surgical robot beyond the resolution of magnetic resonance imaging (MRI) by using a resolution enhancement technique enabled by high-precision piezoelectric actuation. The surgical robot was specifically designed for injecting stem cells into the spinal cord. This particular therapy can be performed in a shorter time by using a MRI-compatible robotic platform than by using a manual needle positioning platform. Imaging resolution of fiducial markers attached to the needle guide tubing was enhanced by reconstructing a high-resolution image from multiple images with sub-pixel movements of the robot. The parallel-plane direct-drive needle positioning mechanism positioned the needle guide with a high spatial precision that is two orders of magnitude higher than typical MRI resolution up to 1 mm. Reconstructed resolution enhanced images were used to navigate the robot precisely that would not have been possible by using standard MRI. Experiments were conducted to verify the effectiveness of the proposed enhanced-resolution image-guided intervention. Copyright © 2020, The Authors. All rights reserved.

关键词： Needles

Evaluation of T-72 tank driver seat based on anthropometric dimensions of Ethiopian military tank drivers

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AIP Conference Proceedings 2022年第1期2648卷

作者： Aliyi Umer Dereje Engida G. M. Sayeed Ahmed U. Pranavi 1College of Engineering Defence University Bishoftu Ethiopia 2Addis Ababa Science and Technology University Addis Ababa Ethiopia 3Program of Mechanical Design and Manufacturing Engineering School of Mechanical Chemical and Materials Engineering (SoMCME) ASTU ADAMA Ethiopia.P.O.Box:1888 4Center of Excellence for Advanced Manufacturing Engineering Program of Mechanical Design Ethiopia.P.O.Box:1888 5Department of Mechanical Engineering Vardhaman College of Engineering Hyderabad-501218 India

In the world it is difficult to design in terms of average dimension. For this reason anthropometric plays a great rule in the world in design of products. In Ethiopia T-72 tanks are the most important weapons that give a strong support to the ground force in the battle field. So the question of the correct ergonomically design and analysis of the driver seat especially in terms of comfort is mandatory so due to this fact, study anthropometric of driver's seat is very important. It is necessary to define the drivers’ space and seat dimensions by considering the size of the average people according to their body dimensions. The basic questions for this research are (i) To what extent the soviet fabricated tank driver's seat is comfortable to Ethiopia army, (ii) Is there significant different between the body size dimension of soviet and Ethiopian army, and (iii) The Ethiopian military derivers have awareness of the comfort. The main goal of this paper is to conduct descriptive survey on an ergonomics assessment of driver's seat of T72 tank. Following sitting height, popliteal height, sitting to shoulder height, shoulder breadth, buttocks to popliteal length, and hip breadth seated anthropometric dimensions were measured of 45 subjects / respondents (35 males, 10 females) randomly selected from the field study. Mean, standard deviation, and T-test were calculated based on anthropometric data of subjects. Results revealed suggest a mismatch among the dimensions of current T-72 Russian fabricated tank motive force seat and the anthropometric dimensions of military staffs. This recommends that anthropometric information of the Ethiopian army tank drivers changed into not working in the design and manufacturing of the seats. This research is may be cautioned that tank driving force seat of T-72 tank and tank drivers anthropometric measurements are at variance. Therefore, the paper presents anthropometric statistics that may be utilized by the Russian manufacturers of tank

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Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular robotics

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arXiv 2025年

作者： Yao, Tianliang Ban, Madaoji Lu, Bo Pei, Zhiqiang Qi, Peng Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electrical and Electronic Engineering Faculty of Engineering The University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Jiangsu Suzhou China School of Oriental Pan-Vascular Devices Innovation College University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes. © 2025, CC BY.

关键词： Reinforcement learning

On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification

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arXiv 2022年

作者： Mu, Biqiang Chen, Tianshi Kong, He Jiang, Bo Wang, Lei Wu, Junfeng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Key Laboratory for NSLSCS of Jiangsu Province School of Mathematical Sciences Nanjing Normal University Nanjing China College of Control Science and Engineering Zhejiang University Hangzhou China

Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error method. For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping. In this paper, we report some new results on the embeddings/inverse embeddings of the QMIE method. Firstly, we present a general result on the frequency domain inverse embedding (FDIE) that is to find the inverse of the quadratic mapping described by the discrete-time Fourier transform. Then we show the relation between the TDIE and the FDIE from a graph signal processing perspective. Finally, motivated by this perspective, we further propose a graph induced embedding and its inverse, which include the previously introduced embeddings as special cases. This deepens the understanding of input design from a new viewpoint beyond the real domain and the frequency domain viewpoints. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

Recent advances in solid-liquid triboelectric nanogenerators for self-powered chemical and biological sensing

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npj Biosensing

npj Biosensing 2024年第1期1卷 1-11页

作者： Kuldeep Kaswan Meenakshi Ray Arshad Khan Zong-Hong Lin Yu-Lin Wang Department of Biomedical Engineering National Taiwan University Taipei 10617 Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu 30013 Taiwan Department of Power Mechanical Engineering National Tsing Hua University Hsinchu 30013 Taiwan Institute of NanoEngineering and MicroSystems National Tsing Hua University Hsinchu 30013 Taiwan

Solid-liquid triboelectric nanogenerators (SL-TENGs) exhibit significant potential in energy harvesting and sensing. This review explores SL-TENG development, focusing on chemical sensing and biosensing applications. Initially, the working mechanisms of various SL-TENG modes are described. Subsequently, an analysis of surface modifications of contact surfaces and liquids to functionalize chemical sensing and biosensing is explored, including their impact on surface properties and the corresponding effect on device performance related to sensing applications.

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Extraction of material properties through multi-fidelity deep learning from molecular dynamics simulation

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arXiv 2020年

作者： Islam, Mahmudul Thakur, Md Shajedul Hoque Mojumder, Satyajit Hasan, Mohammad Nasim Department of Mechanical Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh Theoretical and Applied Mechanics Program Northwestern University EvanstonIL60208 United States

Simulation of reasonable timescales for any long physical process using molecular dynamics (MD) is a major challenge in computational physics. In this study, we have implemented an approach based on multi-fidelity physics informed neural network (MPINN) to achieve long-range MD simulation results over a large sample space with significantly less computational cost. The fidelity of our present multi-fidelity study is based on the integration timestep size of MD simulations. While MD simulations with larger timestep produce results with lower level of accuracy, it can provide enough computationally cheap training data for the MPINN to learn an accurate relationship between these low-fidelity results and high-fidelity MD results obtained using smaller simulation timestep. We have performed two benchmark studies, involving one and two component Lennard-Jones systems, to determine the optimum percentage of high-fidelity training data required to achieve accurate results with high computational saving. The results show that important system properties such as system energy per atom, system pressure and diffusion coefficients can be determined with high accuracy while saving 68% computational costs. Finally, as a demonstration of the applicability of our present methodology in practical MD studies, we have studied the viscosity of argon-copper nanofluid and its variation with temperature and volume fraction by MD simulation using MPINN. Then we have compared them with numerous previous studies and theoretical models. Our results indicate that MPINN can predict accurate nanofluid viscosity at a wide range of sample space with significantly small number of MD simulations. Our present methodology is the first implementation of MPINN in conjunction with MD simulation for predicting nanoscale properties. This can pave pathways to investigate more complex engineering problems that demand long-range MD simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Molecular dynamics

Operations Research and Analytics to combat human trafficking: A systematic review of academic literature

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arXiv 2021年

作者： Dimas, Geri L. Konrad, Renata A. Maass, Kayse Lee Trapp, Andrew C. Data Science Program Worcester Polytechnic Institute United States Business School Worcester Polytechnic Institute United States Mechanical and Industrial Engineering Department Northeastern University United States Business School Worcester Polytechnic Institute Data Science Program Worcester Polytechnic Institute United States

Human trafficking is a widespread and compound social, economic, and human rights issue occurring in every region of the world. While there have been an increasing number of anti-human trafficking works from the Operations Research and Analytics domains in recent years, no systematic review of this literature currently exists. We fill this gap by providing a systematic literature review that identifies and classifies the body of Operations Research and Analytics research related to the anti-human trafficking domain, thereby illustrating the collective impact of the field to date. We classify 142 studies to identify current trends in methodologies, theoretical approaches, data sources, trafficking contexts, target regions, victim-survivor demographics, and focus within the well-established 4Ps principles. Using these findings, we discuss the extent to which the current literature aligns with the global demographics of human trafficking and identify existing research gaps to propose an agenda for Operations Research and Analytics researchers. Copyright © 2021, The Authors. All rights reserved.

关键词： Crime