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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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A Method of Touchable 3d Model Reconstruction based on Mixed Reality -A Case Study of Medical Training Applications 23

A Method of Touchable 3d Model Reconstruction based on Mixed...

引用

6th international conference on Image and Graphics Processing, ICIGP 2023

作者： Liu, Mingyang Guo, Dalei Zhang, Zhicheng The School of Artificial Intelligence University of Chinese Academy of Sciences China The State Key Lab. of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Department of Orthopedic The 7th Medical Center of PLA General Hospital China

ISBN: (纸本)9781450398572

The application of trauma and hemorrhage medical treatment training has generated higher and higher requirements for the authenticity of the medical model. With the increasing integration of mixed reality technology and the medical field, multi-view-based 3D reconstruction technology can generate trauma medical object models not limited to specific targets, with low cost and high precision. However, the current reconstruction methods have weak points such as the inability to build the object surface beyond the direct view of humans, the uneven distribution of reconstructed point clouds, many holes, and reconstructed triangles. And it is time-consuming and laborious to fill and modify manually and requires highly skilled modeling expertise. Therefore, the models generated by traditional 3D reconstruction methods cannot meet the needs of interaction and touch in mixed reality applications. In order to solve these problems, we propose a new model generation method based on multi-view and point cloud processing, which enables the model to reduce the computational cost under the premise of meeting the need for direct interaction and touch in mixed reality applications. Finally, based on the reconstruction method proposed in this paper, a new medical trauma hemorrhage control and rescue training system is designed and implemented, which proves the effectiveness of the method proposed in this paper. © 2023 Owner/Author.

关键词： Mixed reality

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Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics

Learning Dense Visual Correspondences in Simulation to Smoot...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Ganapathi, Aditya Sundaresan, Priya Thananjeyan, Brijen Balakrishna, Ashwin Seita, Daniel Grannen, Jennifer Hwang, Minho Hoque, Ryan Gonzalez, Joseph E. Jamali, Nawid Yamane, Katsu Iba, Soshi Goldberg, Ken Univ Calif Berkeley AUTOLab Berkeley CA 94720 USA Honda Res Inst Columbus OH USA

ISBN: (纸本)9781728190778

Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics. There has been significant prior work on learning policies for specific fabric manipulation tasks, but comparatively less focus on algorithms which can perform many different tasks. We take a step towards this goal by learning point-pair correspondences across different fabric configurations in simulation. Then, given a single demonstration of a new task from an initial fabric configuration, these correspondences can be used to compute geometrically equivalent actions in a new fabric configuration. This makes it possible to define policies to robustly imitate a broad set of multi-step fabric smoothing and folding tasks. The resulting policies achieve 80.3% average task success rate across 10 fabric manipulation tasks on two different physical robotic systems. Results also suggest robustness to fabrics of various colors, sizes, and shapes. See https : / /tinyurl . com/ fabric-descriptors for supplementary material and videos.

关键词： Visualization Smoothing methods Shape Heuristic algorithms Fabrics Robustness Task analysis

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Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments

Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic R...

引用

IEEE conference on Cybernetics and Intelligent Systems

作者： Victor A. Kich Muhammad A. Muttaqien Junya Toyama Ryutaro Miyoshi Yosuke Ida Akihisa Ohya Hisashi Date Intelligent Robot Laboratory University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Recent advancements in real-time object detection frameworks have spurred extensive research into their application in robotic systems. This study provides a comparative analysis of YOLOv5 and YOLOv8 models, challenging the prevailing assumption of the latter’s superiority in performance metrics. Contrary to initial expectations, YOLOv5 models demonstrated comparable, and in some cases superior, precision in object detection tasks. Our analysis delves into the underlying factors contributing to these findings, examining aspects such as model architecture complexity, training dataset variances, and real-world applicability. Through rigorous testing and an ablation study, we present a nuanced understanding of each model’s capabilities, offering insights into the selection and optimization of object detection frameworks for robotic applications. Implications of this research extend to the design of more efficient and contextually adaptive systems, emphasizing the necessity for a holistic approach to evaluating model performance.

关键词： YOLO Training Measurement Adaptation models Analytical models Reliability Robots

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CEPSO: A Novel Clustering Algorithm for Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks in Power Grids

CEPSO: A Novel Clustering Algorithm for Enhancing the Lifeti...

引用

automation, Control and robotics Engineering (CACRE), international conference on

作者： Yuanjiao Zhu Dingyi Jia Tao Luo Baitao Zhang Jingyi Wei Pengwei Ma Yunchen Luo Jie Zhou College of Information Science and Technology Shihezi University Shihezi China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

Facing numerous challenges in traditional Power Grid Wireless Sensor Networks (PGWSNs), including inaccurate node clustering, low energy efficiency, and shortened network lifetime, this study proposes an innovative energy-efficient clustering algorithm-an improved Particle Swarm Optimization algorithm (CEPSO). The algorithm employs a unique elite strategy by constructing a multi-level elite pool to retain outstanding individuals generated during iterations, thereby accelerating the iterative process. To address the complexity and diversity of the power grid environment, the algorithm incorporates Cubic chaotic mapping to enhance performance and adaptability to complex environments. By comparing and analyzing with traditional clustering methods such as LEACH, SEP and standard PSO, the CEPSO algorithm underwent extensive simulation tests on key metrics such as network delay, energy consumption reduction, and network lifetime extension. The research results indicate that the CEPSO algorithm significantly outperforms the compared algorithms in all performance metrics, providing an efficient solution for extending the lifetime of PGWSNs.

关键词： Wireless communication Wireless sensor networks Energy consumption Clustering methods Clustering algorithms Robot sensing systems Energy efficiency

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Modeling and Control of an Untethered Magnetic Gripper

Modeling and Control of an Untethered Magnetic Gripper

引用

IEEE international conference on robotics and automation (ICRA)

作者： Mao, Yunxuan Yuan, Sishen Wang, Jiaole Zhang, Jinmin Song, Shuang Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9781728190778

Small-scale robots have great potential in minimally invasive surgery (MIS). In this paper, we propose an untethered magnetic gripper with small scale and build a double-magnet model for it. The gripper is 4.3mm long and its maximum width is 4mm. It contains a spindle and two magnets, which can achieve precise control of orientation, position and open angle with external magnetic driven field. As a result, it can perform operations such as transporting medicines in confined and constrained environments. Modeling and analysis of the magnetic gripper have been carried out. Relationship between the open angle and external magnetic field has been established. Kinematics model of the gripper has been built. A 3-axis Helmholtz-Maxwell coil system has been established to generate the magnetic field, in which orientation and open angle can be controlled with uniform magnetic field while position can be controlled with gradient field. The proposed gripper have been validated with phantom experiments. An opened angle control error of 0.63 degrees and direction control error of 1.1 degrees have been obtained.

关键词： Analytical models Minimally invasive surgery Magnetic confinement Transportation Phantoms Kinematics Magnetic analysis

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SCAN: Socially-Aware Navigation Using Monte Carlo Tree Search

SCAN: Socially-Aware Navigation Using Monte Carlo Tree Searc...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Jeongwoo Oh Jaeseok Heo Junseo Lee Gunmin Lee Minjae Kang Jeongho Park Songhwai Oh Department of Electrical and Computer Enginnering and ASRI Seoul National University Seoul Korea Sequor Robotics Inc. Seoul Korea Graduate School of Artificial Intelligence (GSAI) and - ASRI Seoul National University Seoul Korea

Designing a socially-aware navigation method for crowded environments has become a critical issue in robotics. In order to perform navigation in a crowded environment without causing discomfort to nearby pedestrians, it is necessary to design a global planner that is able to consider both human-robot interaction (HRI) and prediction of future states. In this paper, we propose a socially-aware global planner called SCAN, which is a global planner that generates appropriate local goals considering HRI and prediction of future states. Our method simulates future states considering the effects of the robot's actions on the future intentions of pedestrians using Monte Carlo tree search (MCTS), which estimates the quality of local goals. For fast simulation, we execute pedestrian motion prediction using Y-net and future state simulation using MCTS in parallel. Neural networks are only used in Y-net and not in MCTS, which enables fast simulation and prediction of a long horizon of future states. We evaluate the proposed method based on the proposed socially-aware navigation metric using realistic pedestrian simulation and real-world experiments. The results show that the proposed method outperforms existing methods significantly, indicating the importance of considering human-robot interaction for socially-aware navigation.

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The PCA Fault Diagnosis Method Based on Causal Relationships

The PCA Fault Diagnosis Method Based on Causal Relationships

引用

automation, robotics and Computer Engineering (ICARCE), international conference on

作者： Changqing Li Jie Liu Xiaohua Yang Ying Wei Zhiqiang Wu Meng Li School of Computer Science University of South China Hengyang Hunan China China Nuclear Power Institute Research and Design Institute Chengdu Sichuan China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

Fault diagnosis is a critical and central issue in industrial engineering, essential for addressing problems encountered in engineering systems. In recent years, data-driven fault diagnosis methods have gained extensive attention and application. Traditional fault diagnosis techniques primarily focus on analyzing data from individual fault nodes, making it challenging to capture the causal relationships between fault nodes accurately. This paper proposes a fault diagnosis method based on the combination of principal component analysis (PCA) and Liang-Kleeman Information Flow (LKIF). This method quantifies causal interactions between nodes by introducing information flow, thereby enhancing diagnostic efficiency. To validate the effectiveness of this approach, we conducted simulation experiments using the Tennessee Eastman Process (TEP) as a testbed. Specifically, we analyzed two faults in the TE platform and designed comparative experiments by varying two input variables. The results demonstrate that the LKIF-PCA fault diagnosis method achieves high accuracy, indicating its potential for practical applications.

关键词： Fault diagnosis automation Accuracy Fault detection Input variables Computational modeling Industrial engineering Robots Principal component analysis

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Solution for Agri Disaster Using Climate Changes Adaptation by Ml 6

Solution for Agri Disaster Using Climate Changes Adaptation ...

引用

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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H∞ Fuzzy Reliable Tracking Controller Design for Nonlinear Systems

H∞ Fuzzy Reliable Tracking Controller Design for Nonlinear ...

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international conference on Autonomous Robots and Agents, ICARA

作者： Mourad Kchaou Badr Alshammari Electrical Engineering Department University of Hail

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

This paper addresses the problem of fault-tolerant tracking controller design for for non-linear systems subject to external disturbances, uncertainties, and actuator failures. The approach is built upon three main aspects: 1) Utilising Takagi-Sugeno (TS) fuzzy models to handle nonlinear systems, along with a non-linear failure model to describe actuator failures. 2) Investigating the fuzzy Lyapunov function to establish less conservative criteria for analysing $H_{\infty}$ tracking performance. 3) Employing linear matrix inequality techniques, with the help of slack matrices to design the controller gains. Finally, the reliability and availability of the presented control method are validated by a Duffing forced oscillation system

关键词： Analytical models Actuators Uncertainty Reliability engineering Linear matrix inequalities Takagi-Sugeno model Nonlinear systems

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Animal image recognition system based on Yolov8-AFPN

Animal image recognition system based on Yolov8-AFPN

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2024 international conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Li, Ning School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Object detection technology has been widely applied in the conservation and monitoring of wildlife in recent years. However, due to the complexity of field environments, the collected images of wild animals often suffer from severe lighting effects, partial blurriness, and significant environmental differences. These issues greatly impact the efficiency and accuracy of object recognition. To overcome these challenges, this paper employs Gaussian filtering as an image enhancement technique and introduces an Asymptotic Feature Pyramid Network (AFPN) to improve the YOLOv8 model. The AFPN extracts features from the bottom up and uses adaptive spatial fusion operations to optimize the feature fusion process, significantly enhancing the detection performance of animal image recognition models. © 2025 SPIE.

关键词： Image enhancement

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