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

共 24757 条记录，以下是611-620 订阅

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Diet Planning models Based on Linear Programming Theory for Catering Problems 2

Diet Planning Models Based on Linear Programming Theory for ...

引用

2nd international conference on automation, robotics and Computer Engineering, ICARCE 2023

作者： Guo, Lianjun Zhang, Jiabo Yu, Jiayuan Wang, Gang Qi, Yunfei Liu, Tao Eighth Geological Brigade of Hebei Bureau of Geology and Mineral Resources Exploration Qinhuangdao China Marine Ecological Restoration and Smart Ocean Engineering Research Center of Hebei Province Qinhuangdao China School of Mathematics and Statistics Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (纸本)9798350308341

As residents attach more and more importance to healthy diet, the catering problem has garnered widespread public attention. Nutrient-based dietary planning can give viable dietary recommendations for the above problems. In this paper, this study establishes a dietary planning model to find the minimum dietary expenditure under the condition that the daily nutritional requirements of diabetic patients are met and the upper limit of nutrient intake is not exceeded. Employing a dependable mathematical model, we make viable planning for diverse social groups and varied food materials, so as to give viable recommendations to the greatest extent. © 2023 IEEE.

关键词： Linear programming

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robotics and AI in Enhancing Banking Operations Efficiency 2

Robotics and AI in Enhancing Banking Operations Efficiency

引用

2nd international conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Arangarajan, M. Gaikwad, Kunal D Dharmani, Meher Nathani, Riya Joshi, Rahul Hasan, Mohammed Faez Shri Angalamman College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Tiruchirappalli621106 India Mgsms Arts Science and Commerce College Department of Electronics Jalgaon India Uks Institute of Management Studies and Research Mumbai India Kishinchand Chellaram College Mumbai India SMeH Manav Rachna International Institute of Research and Studies Department of Journalism & Mass Communication Haryana India Kerbala University Finance Finance and Banking Department Iraq

ISBN: (纸本)9798350364699

The financial sector is experiencing a transformation as a result of the incorporation of AI and robotics into banking operations, which has improved accuracy, efficiency, and client satisfaction. The utilization of robotics, particularly robotic process automation (RPA), simplifies tasks such as data input, transaction processing, and compliance reporting. These operations are enhanced by artificial intelligence (AI) tools, such as machine learning and natural language processing, which enable well-informed decision-making, predictive modeling, and improved data analytics. This investigation investigates the numerous methods by which artificial intelligence (AI) and robotics are influencing the banking sector, particularly in terms of operational efficiency. It also offers a glimpse into the future prospects of this rapidly changing field. © 2024 IEEE.

关键词： Modeling languages

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ReDUCE: Reformulation of Mixed Integer Programs using Data from Unsupervised Clusters for Learning Efficient Strategies 39

ReDUCE: Reformulation of Mixed Integer Programs using Data f...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lin, Xuan Fernandez, Gabriel, I Hong, Dennis W. Univ Calif Los Angeles Dept Mech & Aerosp Engn Robot & Mech Lab Los Angeles CA 90095 USA

ISBN: (纸本)9781728196817

Mixed integer convex and nonlinear programs, MICP and MINLP, are expressive but require long solving times. Recent work that combines learning methods on solver heuristics has shown potential to overcome this issue allowing for applications on larger scale practical problems. Gathering sufficient training data to employ these methods still present a challenge since getting data from traditional solvers are slow and newer learning approaches still require large amounts of data. In order to scale up and make these hybrid learning approaches more manageable we propose ReDUCE, a method that exploits structure within small to medium size datasets. We also introduce the bookshelf organization problem as an MINLP as a way to measure performance of solvers with ReDUCE. Results show that existing algorithms with ReDUCE can solve this problem within a few seconds, a significant improvement over the original formulation. ReDUCE is demonstrated as a high level planner for a robotic arm for the bookshelf problem.

关键词： automation Training data Clustering algorithms Organizations Manipulators Hybrid learning

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A Recurrent Neural Network Based Prediction Method for Continuous Joint Angle Movement 13

A Recurrent Neural Network Based Prediction Method for Conti...

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13th IEEE international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Su, Juhua Cheng, Tao Tan, Xiaowei Li, Mengze Zhang, Bi Zhao, Xingang College of Information Science and Engineering Northeastern University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences State Key Lab of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350315196

Motion intention recognition has been widely used in fields such as robotics and medical assistance. However, traditional non-electromyographic signal cannot meet the demands of certain special scenarios, which limits the development of robots that rely on human-machine interaction. In this paper, we research an RNN algorithm that allows for online parameter tuning, which adopts LSTM and GRU models to predict joint angle changes based on surface electromyography (sEMG) and joint angle information. Moreover, we have identified optimal parameters through extensive experimentation and validated the significant impact of different model parameters on prediction performance. © 2023 IEEE.

关键词： Long short-term memory

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Free-Form Instruction Guided Robotic Navigation Path Planning with Large Vision-Language Model 17th

Free-Form Instruction Guided Robotic Navigation Path Plannin...

引用

17th international conference on Intelligent robotics and Applications

作者： Du, Yuhao Wu, Chengzhong Feng, Mingtao Luo, Jianqiao Zhong, Hang Miao, Zhiqiang Wang, Yaonan Xidian Univ Xian Peoples R China Jiangxi Prov Commun Terminal Ind Co Ltd Xian Peoples R China Hunan Univ Changsha Peoples R China

ISBN: (纸本)9789819607884;9789819607891

The integration of multiple pre-trained models in robotic navigation has the advantage of combining diverse strengths, leading to robust and generalized performance. However, the effectiveness of these models is often limited by path planning strategies, necessitating improvements in navigation capabilities. To overcome this, we introduce the Free-form Instruction Guided Robotic Navigation Path Planning with Large Vision-Language Model (FIG-RN). This model leverages free-form instructions to extract landmarks and directional cues, utilizing a pre-trained visual-language model to associate these landmarks with map nodes, thereby laying the groundwork for subsequent path planning. It evaluates landmark-node matches, node accessibility, and orientation to optimize path planning. Compared to traditional models, FIG-RN offers significant benefits: (i) it requires no map annotations due to its use of high-quality pre-trained models, (ii) it maximizes information use from instructions for better path efficacy, and (iii) it refines visual-language model matching values for improved local navigation. Experimentally, FIG-RN outperforms LM-Nav in success rate, efficiency, and accuracy, with improvements of 0.2, 0.2143, and 0.208, respectively.

关键词： Robotic Navigation Path Planning Pre-trained models Free-form Instructions

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Study of output mechanical energy, developed by a small permanent magnet direct current motor, using simulations of linear motor models

Study of output mechanical energy, developed by a small perm...

引用

2023 international conference Automatics and Informatics, ICAI 2023

作者： Popov, Nickolay Lilov, Stoyan Shopov, Ventseslav Markova, Vanya Institute of Robotics Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9798350312911

Thematic is in the mechatronics and automation branches, appliccable in the robotics. The article analyses: the output mechanical energy of direct-current motor at motion, as well as how it corresponds to the mechanical and electrical parameters, when they are used for a mechatronics system of a robot. The goal criterion is the maximum achievable output mechanical power. This research is applicable as a study of direct-current motor and simulative model in some experiments on mechatronics and automatics control systems. © 2023 IEEE.

关键词： Linear motors

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Multirobot control with double-integrator dynamics and control barrier functions for deformable object transport 39

Multirobot control with double-integrator dynamics and contr...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Herguedas, Rafael Aranda, Miguel Lopez-Nicolas, Gonzalo Sagues, Carlos Mezouar, Youcef Univ Zaragoza Inst Invest Ingn Aragon Zaragoza Spain Univ Clermont Auvergne Clermont Auvergne INP CNRS Inst Pascal F-63000 Clermont Ferrand France

ISBN: (纸本)9781728196817

In this paper, we propose a formation control system for deforming and transporting simultaneously a deformable object with a team of robots, modeled with doubleintegrator dynamics. The goal is to reach a target configuration, defined as a combination of shape, scale, orientation and position of the formation. We augment this controller with a set of control barrier functions (CBFs). The CBFs allow us to satisfy fundamental constraints for the success of the task: avoidance of agent-to-agent, agent-to-obstacle and objectto-obstacle collisions, and of excessive stretching. We test the performance of our proposal in different simulation scenarios.

关键词： Deformable models Shape Simulation Perturbation methods Formation control Proposals Multi-robot systems

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Meta-confidence estimation for stereo matching 39

Meta-confidence estimation for stereo matching

引用

IEEE international conference on robotics and automation (ICRA)

作者： Kim, Seungryong Poggi, Matteo Kim, Sunok Sohn, Kwanghoon Mattoccia, Stefano Korea Univ Seoul South Korea Univ Bologna Bologna Italy Korea Aerosp Univ Goyang South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9781728196817

We propose a novel framework to estimate the confidence of a disparity map taking into account, for the first time, the uncertainty affecting the confidence estimation process itself. Conversely to other tasks such as disparity estimation, the uncertainty of confidence directly hints that the confidence should be increased if initially low, but with high uncertainty, decreased otherwise. By modelling such a cue in the form of a second-level confidence, or meta-confidence, our solution allows for finding incorrect predictions inferred by confidence estimator and for learning a correction for them. Our strategy is suited for any state-of-the-art method known in literature, either implemented using random forest classifiers or deep neural networks. Especially, for deep neural networksbased models, we present a multi-headed confidence estimator followed by an uncertainty network, so as to predict mean confidence and meta-confidence within a single network without the cost of lower accuracy, a known limitation in literature for uncertainty estimation. Experimental results on a variety of stereo algorithms and confidence estimation models prove that the modeled meta-confidence is meaningful of the reliability of the estimated confidence and allows for refining it.

关键词： Deep learning Uncertainty Refining Neural networks Estimation Predictive models Prediction algorithms

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Forward models That Integrate High-Dimensional and Localized Sensing of Peripheral Muscle Behavior Enable Task-Independent Prediction of Lower-Limb Joint Torque and Position Future States 39

Forward Models That Integrate High-Dimensional and Localized...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Rabe, Kaitlin G. Fey, Nicholas P. Univ Texas Austin Dept Biomed Engn Austin TX 78712 USA Univ Texas Austin Walker Dept Mech Engn Austin TX 78701 USA

ISBN: (纸本)9781728196817

We develop a task-independent predictive framework that estimates hip, knee and ankle future behavior from sonomyographic sensing of quadriceps musculature. Two regression models, support vector regression and Gaussian process regression, were trained and tested such that no ambulation mode recognition was required. Sonomyography features of the anterior thigh musculature were extracted during the swing phase of level, incline and stair ambulation tasks as inputs to the two models for continuous prediction of the future stance phase hip, knee and ankle moments. Next, sonomyography features of the anterior thigh musculature were extracted during the stance phase and used to predict the following swing phase hip, knee and ankle angles. Leave-one-stride-out cross-validation is used to evaluate this continuous prediction framework. Additionally, initial, peak and terminal joint moment and angle parameters are extracted from trajectories and evaluated. Both regression models were able to accurately predict continuous future joint moments and angles, as well as initial, peak and terminal value parameters of future joint moments and angles. However, the support vector regression model required relatively lower computational cost. Thus, we recommend the support vector regression model as an optimal model for forward prediction of joint mechanics from sonomyographic sensing during ambulation.

关键词： Machine Learning for Robot Control Prosthetics and Exoskeletons Sensor-Based Control

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Variable Rate Compression for Raw 3D Point Clouds 39

Variable Rate Compression for Raw 3D Point Clouds

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IEEE international conference on robotics and automation (ICRA)

作者： Al Muzaddid, Md Ahmed Beksi, William J. Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

ISBN: (纸本)9781728196817

In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated point clouds of varying quality. In contrast, our network is capable of explicitly processing point clouds and generating a compressed description at a comprehensive range of bitrates. Furthermore, our approach ensures that there is no loss of information as a result of the voxelization process and the density of the point cloud does not affect the encoder/decoder performance. An extensive experimental evaluation shows that our model obtains state-of-the-art results, it is computationally efficient, and it can work directly with point cloud data thus avoiding an expensive voxelized representation.

关键词： RGB-D Perception Deep Learning for Visual Perception Big Data in robotics and automation

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