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IEEE International Conference on robotics and Automation (ICRA)

作者： Yi Feng Bohuan Xue Ming Liu Qijun Chen Rui Fan Robotics & Artificial Intelligence Laboratory (RAIL) the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems the College of Electronic & Information Engineering Tongji University Shanghai P. R. China Department of Computer Science & Engineering the Hong Kong University of Science and Technology (Guangzhou) Hong Kong SAR P. R. China Robotics & Autonomous Systems Thrust of the Systems Hub the Hong Kong University of Science and Technol-ogy (Guangzhou) Nansha Guangzhou P. R. China

Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer from an unsatisfactory trade-off between efficiency and accuracy. To resolve this dilemma, this paper first presents a superfast depth-to-normal translator (D2NT), which can directly translate depth images into surface normal maps without calculating 3D coordinates. We then propose a discontinuity-aware gradient (DAG) filter, which adaptively generates gradient convolution kernels to improve depth gradient estimation. Finally, we propose a surface normal refinement module that can easily be integrated into any depth-to-normal SNEs, substantially improving the surface normal estimation accuracy. Our proposed algorithm demonstrates the best accuracy among all other existing real-time SNEs and achieves the SoTA trade-off between efficiency and accuracy.

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Thermal Casting for Monolithic Soft Actuators

Thermal Casting for Monolithic Soft Actuators

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IEEE International Conference on robotics and Biomimetics

作者： Huacen Wang Boyu Yang Junling Mei Yujin Dai Yixuan Xia Hongqiang Wang Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Guangzhou China

ISBN: (纸本)9781665481106

Soft robotics has significantly impacted many fields in the last decades. However, their current fabrication methodologies are limited by the assemble complexity, limited materials, or long duration. A monolithic thermal casting method for fabrication of elastomer and soft robotics based on thermoset polymer is introduced. In this work, we used simple heating cores to manufacture elastomers and soft actuators. By controlling the heating current and duration, we can adjust the thermal field and consequently change the configuration of the generated elastomer. Due to its short duration (several minutes), this method can benefit the mass production of soft robots for high efficiency. Also, this work offers a new way to design soft robotics without complex molds.

关键词： Fabrication Actuators Casting Spirals Wires Metals Soft robotics

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Real-time attitude tracking of capsule endoscope based on MEMS IMU and error analysis

Real-time attitude tracking of capsule endoscope based on ME...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Huang, Zhuokang Hu, Chengzhi Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Shenzhen518055 China Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

Wireless capsule endoscope (WCE) has been increasingly exploited for noninvasive screening and diagnosis of the entire esophagus, stomach, colon, and small intestine. WCE allows direct visualization of gastrointestinal tracts and causes less pain to the patients. Automated navigation of WCE during the medical procedure will significantly reduce the testing time and improve the usage and efficiency of medical infrastructure. Attitude tracking of WCE is of great importance for real-time tracking of the position and orientation of capsule endoscope. In this paper, a method for calculating the attitude of WCE based on MEMS IMU (Inertial Measurement Units) is implemented. The accuracy of attitude tracking is measured by a series of experiments. By reorientating the capsule endoscope with a high precision 3D rotary stage, the error from the proposed algorism is determined. The experimental results show that the attitude of WCE based on MEMS IMU can meet the diagnostic requirements in about six minutes, but the performance cannot be maintained due to error accumulation. Additionally, we propose a method to improve the attitude accuracy by reciprocating the rotational motion during the inspection process. © 2021 IEEE.

关键词： Endoscopy

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Toward Process Controlled Medical Robotic System

arXiv

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arXiv 2023年

作者： Liu, Yihao Kheradmand, Amir Armand, Mehran The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States The Department of Neurology Department of Neuroscience Laboratory for Computational Sensing & Robotics Johns Hopkins University BaltimoreMD21218 United States The Biomechanical- and Image-Guided Surgical Systems Laboratory Laboratory for Computational Sensing & Robotics Department of Orthopaedic Surgery Department of Mechanical Engineering Department of Computer Science Applied Physics Laboratory Johns Hopkins University BaltimoreMD21218 United States

Medical errors, defined as unintended acts either of omission or commission that cause the failure of medical actions, are the third leading cause of death in the United States. Medical errors can include communication breakdowns, diagnostic errors, poor judgment, and inadequate skills. The application of autonomy and robotics can alleviate some causes of medical errors by improving accuracy and providing means to accurately follow planned procedures. However, for the robotic applications to improve safety, they must maintain constant operating conditions in the presence of disturbances, and provide reliable measurements, evaluation, and control for each state of the procedure. This article addresses the need for process control in medical robotic systems, and proposes a standardized design cycle toward its automation. Monitoring and controlling the changing conditions in a medical or surgical environment necessitates a clear definition of workflows and their procedural dependencies. We propose integrating process control into medical robotic workflows using hierarchical Finite State Machines (hFSM) to identify changes in states of the system and environment, and execute possible operations or transitions to new states. Therefore, the system translates clinician experiences and procedure workflows into machine-interpretable languages. The design cycle using hFSM formulation can be a deterministic process, which opens up possibilities for higher-level automation in medical robotics. Shown in our work, with a standardized design cycle and software paradigm, we pave the way toward controlled workflows that can be automatically generated. Additionally, a modular design for a robotic system architecture that integrates hFSM can provide easy software and hardware integration. This article discusses the system design, software implementation, and example application to Robot-Assisted Transcranial Magnetic Stimulation (RATMS) and robot-assisted femoroplasty. We also provide

关键词： Navigation systems

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E-3DGS: Gaussian Splatting with Exposure and Motion Events

arXiv

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arXiv 2024年

作者： Yin, Xiaoting Shi, Hao Bao, Yuhan Bing, Zhenshan Liao, Yiyi Yang, Kailun Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University China School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China College of Information Science and Electronic Engineering Zhejiang University China The Chair of Robotics AI and Real-Time Systems Technical University of Munich Germany

Estimating Neural Radiance Fields (NeRFs) from images captured under optimal conditions has been extensively explored in the vision community. However, robotic applications often face challenges such as motion blur, insufficient illumination, and high computational overhead, which adversely affect downstream tasks like navigation, inspection, and scene visualization. To address these challenges, we propose E-3DGS, a novel event-based approach that partitions events into motion (from camera or object movement) and exposure (from camera exposure), using the former to handle fast-motion scenes and using the latter to reconstruct grayscale images for high-quality training and optimization of event-based 3D Gaussian Splatting (3DGS). We introduce a novel integration of 3DGS with exposure events for high-quality reconstruction of explicit scene representations. Our versatile framework can operate on motion events alone for 3D reconstruction, enhance quality using exposure events, or adopt a hybrid mode that balances quality and effectiveness by optimizing with initial exposure events followed by high-speed motion events. We also introduce EME-3D, a real-world 3D dataset with exposure events, motion events, camera calibration parameters, and sparse point clouds. Our method is faster and delivers better reconstruction quality than event-based NeRF while being more cost-effective than NeRF methods that combine event and RGB data by using a single event sensor. By combining motion and exposure events, E-3DGS sets a new benchmark for event-based 3D reconstruction with robust performance in challenging conditions and lower hardware demands. The source code and dataset will be available at https://***/MasterHow/E-3DGS. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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IIPC: Intra-Inter Patch Correlations for Garment Collision Handling

IIPC: Intra-Inter Patch Correlations for Garment Collision H...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ruisheng Yuan Minzhe Tang Dongliang Kou Mingyang Sun Dingkang Yang Xiao Zhao Lihua Zhang Academy for Engineering and Technology Fudan University Shanghai China Jilin Provincial Key Laboratory of Intelligence Science and Engineering Changchun China Ministry of Education Engineering Research Center of AI and Robotics Shanghai China AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Realistic garment simulation is critical for digital humans. However, noticeable penetrations still exist in current learning-based garment simulation techniques. To reduce penetrations in predicted garments, we resort to the garment geometry and neural Signed Distance Fields (SDFs) for effective collision handling. The key idea of our method is that we divide the garment into patches and model the local and global garment geometry through Intra- and Inter-Patch Correlations (IIPC), which can be easily learned through the powerful context-understanding ability of Transformers. The geometry information is then utilized to predict a per-vertex moving offset, according to which we move the penetrating vertices along the SDF’s gradient directions to solve collisions. Our module can be coupled with learning-based backbones to effectively solve penetrations while retaining real-time performance. Extensive experiments show that the proposed method excels the prior works significantly.

关键词： Geometry Correlation Art Clothing Predictive models Transformers Real-time systems Digital humans Context modeling

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Leg Transmission Mechanism for Enhanced Load-Carrying Capacity: Knee Joint Torque Optimization Design

Leg Transmission Mechanism for Enhanced Load-Carrying Capaci...

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IEEE International Conference on Development and Learning, ICDL

作者： Quan Huang Yixuan Guo Haoyun Yan Yuquan Leng Shoubin Liu Chenglong Fu School of Mechanical Engineering and Automation Harbin Institute of Technology 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 Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

This study proposes an optimized design for the leg transmission structure of a quadruped robot, aiming to enhance the load-bearing capacity of the knee joint. Firstly, the dynamic simulation of the single leg's load-lifting process in the quadruped robot is conducted, indicating higher torque demands on the knee joint during this process. Therefore, a leg transmission mechanism, combining a ball screw mechanism and a crank-slider mechanism, is designed to effectively amplify the torque of the knee joint motor. Subsequently, a virtual prototype of the quadruped robot, incorporating this design, is constructed using Adams software. The foot trajectory of the robot is pre-planned, and the joint trajectories are obtained through inverse kinematics, using them as the driving functions for the quadruped robot to perform actions such as load lifting, static standing, and diagonal walking gait. Finally, the knee joint torque and corresponding motor driving torque of the quadruped robot's legs are measured and compared. The results demonstrate that the driving torque of the knee joint motor is effectively amplified by the leg transmission mechanism, with the maximum amplification observed during the initial stage of load lifting, reaching a magnification factor of nearly 7.66 times. This verifies the effectiveness of the proposed leg transmission mechanism in enhancing the load-bearing capacity of the knee joint.

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LoRa technology application within the Internet of Flying Things concept for data collection from IoT devices

LoRa technology application within the Internet of Flying Th...

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Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Alexander Dubelschikov Timur Gamberov Tatyana Tsoy Kuo-Hsien Hsia Evgeni Magid Tikhonov Moscow Institute of Electronics and Mathematics HSE University Moscow Russia Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Graduate School Engineering Science and Technology National Yunlin University of Science & Technology Taiwan The Republic of China

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

The Internet of Flying Things is one of the priority areas for robotics and automation research and applications. A promising case is an application of UAVs in a search and rescue scenario for data collection from pre-positioned IoT devices, which allows assessing and monitoring a current situation in an operation area. This paper presents a concept of using a low-power LoRa protocol to transmit data from smart sensors to UAVs that deliver the data to a dedicated cloud for further processing and analysis. A mock-up of a structure, which is described in the paper, is to be realized using microcontrollers, LoRa modules and smart temperature sensors.

关键词： Temperature sensors Protocols Microcontrollers LoRa Data collection Communications technology Internet of Things robotics and automation Monitoring Intelligent sensors

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Modelling and Control of a Trailer Sprayer for Precision Spraying

Modelling and Control of a Trailer Sprayer for Precision Spr...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： André Baltazar Filipe N. Santos Antόnio Paulo Moreira Salviano Pinto Soares M. J. C. S. Reis José Boaventura Cunha CRIIS-Centre for Robotics in Industry and Intelligent Systems INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto Portugal ECT—School of Science and Technology UTAD—University of Trás-os-Montes and Alto Douro Vila Real Portugal FEUP—Faculty of Engineering University of Porto Porto Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Portugal Intelligent Systems Associate Laboratory (LASI) Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Precision spraying in agriculture is crucial for optimizing the application of pesticides while minimizing environmental impact. Despite significant advancements in control models for spraying systems, predictive control algorithms were not used. This paper addresses this gap by proposing a real-time control framework that integrates predictive control strategies to ensure consistent pressure output in a trailer sprayer. Based on information from various sensors, the framework anticipates and adapts to dynamic environmental conditions, enhancing accuracy and sustainability in spraying practices. A methodology is developed to define a proportional valve model. Based on this valve model, the predictive control model optimizes valve movements to minimize errors between predicted and reference pressures, thereby improving spraying efficiency. This study demonstrates the viability of predictive control in improving precision spraying systems applicable to autonomous robots, encouraging future advances in agricultural spraying technologies.

关键词： Adaptation models Spraying Predictive models Valves Real-time systems Agriculture Sensors

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