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检索条件"任意字段=25th International Conference on Methods and Models in Automation and Robotics"

共 1217 条记录，以下是241-250 订阅

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thÖR-Magni: Comparative Analysis of Deep Learning models for Role-conditioned Human Mtion Prediction

THÖR-Magni: Comparative Analysis of Deep Learning Models fo...

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2023 IEEE/CVF international conference on Computer Vision Workshops, ICCVW 2023

作者： De Almeida, Tiago Rodrigues Rudenko, Andrey Schreiter, Tim Zhu, Yufei Maestro, Eduardo Gutierrez Morillo-Mendez, Lucas Kucner, Tomasz P. Martinez Mozos, Oscar Magnusson, Martin Palmieri, Luigi Arras, Kai O. Lilienthal, Achim J. Örebro University Sweden Corporate Research Robert Bosch GmbH Stuttgart Germany Aalto University Mobile Robotics Group Department of Electrical Engineering and Automation Finland Finnish Center for Artificial Intelligence Fcai Finland

ISBN: (纸本)9798350307443

Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel thÖR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics. © 2023 IEEE.

关键词： deep learning human motion dataset human trajectory prediction

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Prediction of Overdispersed Count Data Using Real-Time Cluster-Based Discretization of Explanatory Variables 18th

Prediction of Overdispersed Count Data Using Real-Time Clus...

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18th international conference on Informatics in Control, automation and robotics, ICINCO 2021

作者： Uglickich, Evženie Nagy, Ivan Department of Signal Processing Institute of Information Theory and Automation of the CAS Pod vodárenskou věží 4 Prague18208 Czech Republic Faculty of Transportation Sciences Czech Technical University Na Florenci 25 Prague11000 Czech Republic

ISBN: (纸本)9783031264733

the chapter focuses on the description of the relationship of the count variable and explanatory Gaussian variables. the cluster-based model is proposed, which is constructed on conditionally independent Gaussian clusters captured in real time using recursive algorithms of the Bayesian mixture estimation theory. the resulting model is expected to be used for predicting count data using real time Gaussian observations. the Poisson distribution of the count data is used as a basic model. However, in reality, count data often do not satisfy the Poisson assumption of equal mean and variance. For this case, five cluster-based Poisson-related models of overdispersed data have been studied. the experimental part of the chapter demonstrates a comparison of the prediction accuracy of the considered models with two theoretical counterparts for the case of weak and strong overdispersion with the help of simulations. the paper reports that the most accurate prediction in average has been provided by the cluster-based Generalized Poisson models. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Poisson distribution

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Trajectory Tracking for UAVs: An Interpolating Control Approach

Trajectory Tracking for UAVs: An Interpolating Control Appro...

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international conference on methods and models in automation and robotics (MMAR)

作者： Zdeněk Bouček Miroslav Flídr Ondřej Straka New Technologies for the Information Society Research Center & Department of Cybernetics Faculty of Applied Sciences University of West Bohemia Pilsen Czechia

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Building on our previous work, this paper investigates the effectiveness of interpolating control (IC) for real-time trajectory tracking. Unlike prior studies that focused on trajectory tracking itself or UAV stabilization control in simulation, we evaluate the performance of a modified extended IC (eIC) controller compared to Model Predictive Control (MPC) through both simulated and laboratory experiments with a UAV. the evaluation focuses on the computational efficiency and control quality of real-time UAV trajectory tracking compared to previous IC applications. the results demonstrate that the eIC controller achieves competitive performance compared to MPC while significantly reducing computational complexity, making it a promising alternative for resource-constrained platforms.

关键词： Interpolation Trajectory tracking Computational modeling Predictive models Autonomous aerial vehicles Real-time systems Computational efficiency

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Development of criteria for the selection and investigation of PEMFC stacks as components of hybrid energy sources for UGVs

Development of criteria for the selection and investigation ...

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international conference on methods and models in automation and robotics (MMAR)

作者： Mikołaj Zarzycki Magdalena Dudek Andrzej Raźniak Andrzej Masłowski Arkadiusz Perski Rafał Czupryniak Mobile Systems Department Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP Warsaw Poland Faculty of Energy and Fuels AGH University of Krakow Krakow Poland Science Department Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP Warsaw Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

this paper focuses on developing the methodology for selecting polymer exchange membrane fuel cell (PEMFC) stacks as the main energy source to supply an unmanned ground vehicle (UGV). the first part of the paper presents the experimental investigations of the electrical load profiles recorded for the PIAP PATROL UGV during both vehicle movement and manipulator operation. the numerical simulation of the energy demand profile was performed for various terrains and roads. the 2 kW PEMFC stack is proposed and investigated as an electrochemical energy source to supply the medium-sized tracked vehicle. In addition, the preliminary test of the 2 kW PEMFC stack performance in powering the electric motor while traveling is demonstrated.

关键词： Tracking Roads Hydrogen Fuel cells Numerical simulation Manipulators Land vehicles

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Improved Model of the PWM Driven 3/2 Solenoid Valve Pneumatic System for Soft Pneumatic Actuators

Improved Model of the PWM Driven 3/2 Solenoid Valve Pneumati...

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international conference on methods and models in automation and robotics (MMAR)

作者： Krisjanis Visnevskis Shakiru Olajide Kassim Maria Elena Giannaccini Vahid Vaziri Sumeet S. Aphale Artificial Intelligence Robotics and Mechatronic Systems Group (ARMS) Centre for Advanced Dynamic Research (CADR) School of Engineering University of Aberdeen Aberdeen UK

Soft pneumatic actuators (SPAs) are the building blocks of soft robots that are characterized by their inherent compliance, high degree of dexterity, and safety in human-robot interaction. the non-linear dynamics of the pneumatic system and elastomeric material deformation in SPAs presents a challenge in developing control systems for these actuators. therefore simulations are often used to aid in the design and validation of new control schemes for SPAs. Pressure control is an important aspect of achieving precise motion control of the *** paper presents an improved simulation model for a SPA pneumatic system that utilizes a 3/2 solenoid valve. the model was implemented using the SimScape Fluids toolbox in MATLAB. the proposed simulation model builds on existing models by accounting for the 3/2 solenoid valve dynamics. the open-loop response of the improved model was compared with the previous model and experimental data. the results showed that the improved model was more representative of the real-world performance of the system under open-loop control.

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Research on the Role of MCP Regularization in Bearing Fault Diagnosis

Research on the Role of MCP Regularization in Bearing Fault ...

引用

robotics, Control and automation (ICRCA), international conference on

作者： Hao Tian Jiang Ting Zhang Shanghai University of Engineering Science School of Mechanical and Automotive Engineering Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

In practical applications, the collection of bearing fault data often includes a significant number of invalid samples. When these data are used to train deep learning-based bearing fault diagnosis models, there is a risk of overfitting. Traditional methods, such as L1 and L2 regularization, are commonly employed to mitigate this risk. this paper proposes a novel regularization method based on the Minimax Concave Penalty (MCP) for deep learning models. Experimental validation was conducted using bearing fault data to train these models. Among the three groups of experiments, the highest average fault diagnosis accuracy after MCP regularization was 0.972. the results indicate that the MCP method demonstrates asymptotic normality in updating network weights and exhibits dynamic adaptability in penalizing the loss function. therefore, the method proposed in this paper offers superior overfitting prevention in deep learning applications compared to L1 and L2 regularization.

关键词： Deep learning Fault diagnosis Adaptation models Accuracy Computational modeling Prevention and mitigation Stability analysis Optimization Robots Overfitting

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Application of Bayes theory Model to Determine the Optimal Variant of Technological Process for Production of MEMS Components

Application of Bayes Theory Model to Determine the Optimal V...

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international conference on Perspective Technologies and methods in MEMS Design (MEMSTECH)

作者： Igor Nevliudov Sofiia Khrustalova Olena Chala Andrii Sliusar Department of Computer-Integrated Technologies Automation and Robotics Kharkiv National University of Radio Electronic Kharkiv Ukraine

Bayesian model is proposed for determining the optimal variant of the MEMS component manufacturing technological process A software tool has been developed to automate this process Calculations are performed using the... 详细信息

ISBN: (数字)9798350378627

ISBN: (纸本)9798350378634

关键词： Micromechanical devices Solid modeling Costs Design methodology Production Data models Bayes methods

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Artificial Intelligence models for prediction of passengers’ flow in rail transport

Artificial Intelligence models for prediction of passengers...

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international conference on methods and models in automation and robotics (MMAR)

作者： Maciej Szymkowski Włodzimierz Woźniak Bartosz Jura Faculty of Computer Science Białystok University of Technology Białystok Poland Computer Science Research Group Artificial Intelligence Development division Łukasiewicz Research Network – Poznań Institute of Technology Poznań Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Nowadays, one of the most popular and ecological forms of passengers’ transport is rail. In the last couple of years (especially after COVID-19 pandemic) many more passengers selected this type of transportation rather than cars or even airplanes. It is connected with much higher travel comfort as well as the possibility of effective work on the train. However, due to the higher popularity, one extremely important problem was observed. It is connected with the passengers’ flow that can lead even to traffic. Sometimes it is hard to select an appropriate type of train to effectively serve all the interested people. In this work, we would like to propose not only the survey under the latest AI-based models for prediction of passengers’ pileups but also provide information about possible ideas that will be tested on the real data, collected from one of the trains stations in Poland.

关键词： Rails Surveys COVID-19 Pandemics Biological system modeling Transportation Predictive models

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Real-time Gait Trajectory Prediction Based on Convolutional Neural Network with Multi-head Attention 27

Real-time Gait Trajectory Prediction Based on Convolutional ...

引用

27th international conference on automation and Computing, ICAC 2022

作者： Yin, Jie Xue, Tao Zhang, Tao Tsinghua University Department of Automation Beijing China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

the lower limb exoskeleton can effectively improve the ability of users. Accurate gait trajectory prediction can enhance the effect of lower limb exoskeletons. the current gait trajectory prediction methods have the disadvantages of insufficient prediction accuracy and long calculation time. this paper proposes a convolutional neural network model with multi-head attention to predict the gait trajectory. Compared with the widely used recurrent neural network model, the model we proposed in this paper can predict gait trajectory with higher accuracy and shorter calculation time. the error is reduced by up to 21.3% and the average calculation time is reduced by 62.8%. Based on the proposed model, we design a controller of the lower limb exoskeleton to achieve better effects in mixed gait and eliminate the delay. © 2022 IEEE.

关键词： Neural network models

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Advances in Large Language models for robotics

Advances in Large Language Models for Robotics

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Zongshuai Qi Xiao Jing China Telecom Research Institute Beijing China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

the application of large language models (LLMs) in robotics has garnered significant attention due to their potential to enhance robot understanding, reasoning, and interaction capabilities. this paper provides a comprehensive overview of the current state-of-the-art in integrating LLMs with robotic systems. We categorize and analyze existing methods from both task-oriented and model-oriented perspectives. Task-oriented applications include multi-object rearrangement, representation learning, error correction, and navigation, while model-oriented approaches encompass fine-tuning, direct use, and developing new specialized models. Furthermore, we propose a novel robot embodied intelligence framework that integrates LLMs with the Robot Operating System (ROS) to enable natural language understanding, task planning, execution, and user interaction. this framework aims to enhance robot autonomy and adaptability in unstructured environments, paving the way for new possibilities in real-world robot applications. We discuss the advantages and challenges associated with each approach and provide insights into future research directions in this rapidly evolving domain.

关键词： Representation learning Adaptation models Mechatronics Navigation Large language models Operating systems Natural language processing Planning Error correction Robots

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