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International Symposium on Medical robotics (ISMR)

作者： Chang Chang Junling Mei Yanzhou Wang Lidia Al-Zogbi Simon Leonard Amit Jain Iulian Iordachita Department of Mechanical Engineering Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Orthopaedic Surgery Johns Hopkins University School of Medicine Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

This paper presents a flexible needle guidance system and its workflow that enables registration of computed tomography (CT) and electromagnetic (EM) tracking systems with a finite element (FE) simulator for needle-based percutaneous spinal injections. CT is used only pre- and postoperatively for surgical planning and confirmation, while EM tracking is combined intraoperatively with an FE-based needle controller to track the planned needle trajectory and avoid obstacles. Evaluation of the proposed system using a multi-layer soft tissue phantom shows an average targeting accuracy of 0.4 mm.

关键词： Target tracking Medical robotics Computed tomography Biological tissues Phantoms Needles Trajectory Planning Finite element analysis Electromagnetics

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Fabrication of Sensorsless Automated Guided Vehicle for Small Warehouse Rack Management

Fabrication of Sensorsless Automated Guided Vehicle for Smal...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Neelesh Kumar Sahu Piyush Chaturvedi Vaishali Gupta Ruchi Patel Ashok Verma Megha Shrivastava Artificial Intelligence and Robotics Gyan Ganga Institute of Technology and Sciences Jabalpur MP India Department of Computer Sc. & Engg. IPS Academy Institute of Engineering & Science Indore MP India Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur MP India Department of Mathematics Medicaps University Indore MP India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Automated guided vehicles (AGVs) are an essential part of today's logistics networks because they save time, reduce wear and maintenance expenses, and maximize efficiency in route *** creation of AGVthat is capable of effectively managing warehouse racks without the utilization of sensors is the primary emphasis of this project. In order to travel and carry out duties, the AGVruns on predetermined pathways and makes use of a button-based interface. For the purpose of offering an efficient and costeffective solution for the automation of small warehouses, it is outfitted with a lever mechanism that is operated by a servo motor and features the ability to connect and move racks.

关键词： Fabrication Remotely guided vehicles Automation Solids Sensors Maintenance Information and communication technology Servomotors Logistics

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A Toolkit to Generate Social Navigation Datasets 21st

A Toolkit to Generate Social Navigation Datasets

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21st International Workshop of Physical Agents, WAF 2020

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Information Systems Birla Institute of Technology and Science Goa Pilani India Robotics and Artificial Vision Laboratory University of Extremadura Badajoz Spain Department of Computer Engineering Pune Institute of Computer Technology Pune India Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham United Kingdom

ISBN: (纸本)9783030625788

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Navigation

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Data-Driven Latent Space Representation for Robust Bipedal Locomotion Learning

Data-Driven Latent Space Representation for Robust Bipedal L...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Guillermo A. Castillo Bowen Weng Wei Zhang Ayonga Hereid Electrical and Computer Engineering Ohio State University Columbus OH USA Department of Computer Science Iowa State University Ames IA USA SUSTech Institute of Robotics Southern University of Science and Technology (SUSTech) China Mechanical and Aerospace Engineering Ohio State University Columbus OH USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel framework for learning robust bipedal walking by combining a data-driven state representation with a Reinforcement Learning (RL) based locomotion policy. The framework utilizes an autoencoder to learn a low-dimensional latent space that captures the complex dynamics of bipedal locomotion from existing locomotion data. This reduced dimensional state representation is then used as states for training a robust RL-based gait policy, eliminating the need for heuristic state selections or the use of template models for gait planning. The results demonstrate that the learned latent variables are disentangled and directly correspond to different gaits or speeds, such as moving forward, backward, or walking in place. Compared to traditional template model-based approaches, our framework exhibits superior performance and robustness in simulation. The trained policy effectively tracks a wide range of walking speeds and demonstrates good generalization capabilities to unseen scenarios.

关键词： Legged locomotion Training Manifolds Supervised learning Reinforcement learning Aerospace electronics Robustness

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RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion

arXiv

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arXiv 2024年

作者： Li, Zhiyuan Zhou, Yanhui Wei, Hao Ge, Chenyang Mian, Ajmal Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Computer Science and Software Engineering The University of Western Australia Crawley PerthWA6009 Australia

Diffusion-based extreme image compression methods have achieved impressive performance at extremely low bitrates. However, constrained by the iterative denoising process that starts from pure noise, these methods are limited in both fidelity and efficiency. To address these two issues, we present Relay Residual Diffusion Extreme Image Compression (RDEIC), which leverages compressed feature initialization and residual diffusion. Specifically, we first use the compressed latent features of the image with added noise, instead of pure noise, as the starting point to eliminate the unnecessary initial stages of the denoising process. Second, we directly derive a novel residual diffusion equation from Stable Diffusion’s original diffusion equation that reconstructs the raw image by iteratively removing the added noise and the residual between the compressed and target latent features. In this way, we effectively combine the efficiency of residual diffusion with the powerful generative capability of Stable Diffusion. Third, we propose a fixed-step fine-tuning strategy to eliminate the discrepancy between the training and inference phases, thereby further improving the reconstruction quality. Extensive experiments demonstrate that the proposed RDEIC achieves state-of-the-art visual quality and outperforms existing diffusion-based extreme image compression methods in both fidelity and efficiency. The source code and pre-trained models will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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Humanoid Pose Estimation through Synergistic Integration of computer Vision and Deep Learning Techniques*

Humanoid Pose Estimation through Synergistic Integration of ...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Chaithra Lokasara Mahadevaswamy Jacky Baltes Hsien-Tsung Chang Department of Artificial Intelligence Chang Gung University Taiwan Department of Electrical Engineering Educational Robotics Center National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering Artificial Intelligence Chang Gung University Taoyuan Taiwan

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

This study explores the performance of Convolutional Neural Networks (CNNs) in the context of humanoid robot localization in dynamic environments. Utilizing a front-mounted camera system, initial experiments demonstrate CNNs achieving a $72 \%$ accuracy in position and a $92 \%$ accuracy rate in orientation with an 8000-image dataset. These results underscore the effectiveness of CNNs in addressing the challenge of precise robot localization. Moreover, the study introduces the YOLO (You Only Look Once) object detection algorithm to further enhance performance. Beyond robotics, this research extends to applications in smartphone navigation, Indoor GPS systems, and drone tracking. The paper provides insights into the methodologies employed and highlights the transformative potential of integrating CNNs into localization tasks.

关键词： YOLO Location awareness Accuracy Navigation Humanoid robots computer architecture Robot localization Convolutional neural networks Global Positioning System Drones

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor 16

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Park, Nu-Ri Kim, Min-Joong Lee, Jongsu Seo, Minah Ryu, Yong-Sang Kim, Myung-Ki KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Korea Institute of Science and Technology Sensor System Research Center Seoul02792 Korea Republic of Northwestern University Department of Electrical Engineering and Computer Science EvanstonIL60208 United States School of Biomedical Engineering Korea University Seoul02481 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02481 Korea Republic of

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen ( ©... 详细信息

ISBN: (纸本)9798350372076

关键词： Nanoclay

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Robots Taking Initiative in Collaborative Object Manipulation: Lessons from Physical Human-Human Interaction

arXiv

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arXiv 2023年

作者： Rysbek, Zhanibek Oh, Ki Hwan Shervedani, Afagh Mehri Klemenčič, Timotej Žefran, Miloš Di Eugenio, Barbara Robotics Lab Department of Electrical and Computer Engineering The University of Illinois at Chicago ChicagoIL60607 United States Laboratory of Robotics Faculty of Electrical Engineering University of Ljubljana Slovenia Natural Language Processing Lab Department of Computer Science The University of Illinois at Chicago ChicagoIL60607 United States

Physical Human-Human Interaction (pHHI) involves the use of multiple sensory modalities. Studies of communication through spoken utterances and gestures are well established, but communication through force signals is not well understood. In this paper, we focus on investigating the mechanisms employed by humans during the negotiation through force signals, and how the robot can communicate task goals, comprehend human intent, and take the lead as needed. To achieve this, we formulate a task that requires active force communication and propose a taxonomy that extends existing literature. Also, we conducted a study to observe how humans behave during collaborative manipulation tasks. An important contribution of this work is the novel features based on force-kinematic signals that demonstrate predictive power to recognize symbolic human intent. Further, we show the feasibility of developing a real-time intent classifier based on the novel features and speculate the role it plays in high-level robot controllers for physical Human-Robot Interaction (pHRI). This work provides important steps to achieve more human-like fluid interaction in physical co-manipulation tasks that are applicable and not limited to humanoid, assistive robots, and human-in-the-loop automation. Copyright © 2023, The Authors. All rights reserved.

关键词： Human robot interaction

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Graph Neural Networks for Human-Aware Social Navigation 21st

Graph Neural Networks for Human-Aware Social Navigation

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21st International Workshop of Physical Agents, WAF 2020

作者： Manso, Luis J. Jorvekar, Ronit R. Faria, Diego R. Bustos, Pablo Bachiller, Pilar Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham United Kingdom Department of Computer Engineering Pune Institute of Computer Technology Pune India Robotics and Artificial Vision Laboratory University of Extremadura Badajoz Spain

ISBN: (纸本)9783030625788

Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to comply with social rules, such as avoiding the personal spaces of the people surrounding them, or not getting in the way of human-to-human and human-to-object interactions. This paper suggests using Graph Neural Networks to model how inconvenient the presence of a robot would be in a particular scenario according to learned human conventions so that it can be used by path planning algorithms. To do so, we propose two automated scenario-to-graph transformations and benchmark them with different Graph Neural Networks using the SocNav1 dataset [1]. We achieve close-to-human performance in the dataset and argue that, in addition to its promising results, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered and easily embedded in code in comparison with model-based approaches. The code used to train and test the resulting graph neural network is available in a public repository. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human robot interaction

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