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

共 24622 条记录，以下是4731-4740 订阅

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A Collaborative Robot Torque Prediction Method Based on CNN-TCN Model

A Collaborative Robot Torque Prediction Method Based on CNN-...

引用

IEEE international conference on Real-time Computing and robotics (RCAR)

作者： Lina Tong Decheng Cui Chen Wang Liang Peng School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems (SKLMCCS) Institute of Automation Chinese Academy of Science Beijing China

The traditional dynamical models show lower accuracy when predicting joint movement, and should be compensated. This paper proposed a model combined with the convolutional network(CNN) and temporal convolutional network(TCN) to compensate for the joint torque prediction values that are calculated from the sensing information. The experiments on the Cooperative Universal Robotic Assistant 6 DoF(CURA6) open dataset, including multi-load and multi-velocity, showed the prediction error can be reduced by 20% compared to other network models. Since there are many kinds of joint movement information, the input data form of the deep learning model should be improved. Thus, the kinetic linearization model is proposed to modify the input of sensing data. According to the different motion types of the CURA6 dataset, comparative experiments were taken, and the mean absolute error was less than 6.8%.

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Spectral-domain Spatial-temporal Convolution Graph Neural Network for Industrial Fault Diagnosis 3

Spectral-domain Spatial-temporal Convolution Graph Neural Ne...

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2023 3rd international conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2023

作者： Ru, Jiapei Zeng, Wei School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei China

In modern industrial production, once a fault occurs in the production process, it will affect the whole production and bring huge economic losses. There are a large number of device sensors in modern production, and there are complex interactions between them. But the existing methods can not efficiently and accurately mine the depth of information from mass production data. We proposed the Spectral-domain Spatial-Temporal Convolution Graph Neural Network model, which comprehensively considers the information of spatial and temporal dimensions in production data. The nodes and edges of the heterogeneous graph represent the sensor signals and their interactions. Each sensor data is converted to the spectral domain to extract the time domain features, and graph convolution is used to carry out the spatial relationship between input sequences. Finally, the proposed model is verified using the Three-Phase Flow Facility (TFF) dataset, and the overall accuracy rate reached 96.28%, showing better performance than other baseline models. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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A Cerebellar Model Articulation Controller Enhanced Neural Solution to Time-Varying Linear Equations With Robotic Application

A Cerebellar Model Articulation Controller Enhanced Neural S...

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Intelligent robotics and Automatic Control (IRAC), international conference on

作者： Weibing Li Yuxuan Chen Xin Ma School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

Time-varying linear equations (TVLEs) are common in practice. The existing TVLE solvers like the gradient-dynamics based neurodynamic networks (GDNNs) unavoidably introduce inevitable errors. In contrast, Getz-Marsden dynamic inverters (GMDIs) and error-dynamics based neurodynamic networks (EDNNs) require time derivatives of the coefficients and matrix inversion. To circumvent these limitations, a cerebellar model articulation controller (CMAC) enhanced GDNN (CMAC-GDNN) is proposed, where CMAC serves as a derivative estimator inspired by the fine-tuning correction mechanism for motion signals of the cerebellum. The CMAC-GDNN solver integrates GDNN and CMAC models that generate approximate solutions and correction signals, respectively. The CMAC-GDNN solver possesses some advantages of GMDI and EDNN solvers, avoiding complexities associated with the time derivatives and matrix inversion. In this paper, the structure of the CMAC-GDNN solver is presented in detail with its effectiveness and practicality demonstrated through a numerical example and a motion planning task in which a robot tracks a specific path.

关键词： Neurodynamics Robot motion Accuracy Tracking Brain modeling Mathematical models Planning Trajectory Joints Robots

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Uncertainty-aware Navigation in Crowded Environment

Uncertainty-aware Navigation in Crowded Environment

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Emmanuel Alao Philippe Martinet ACENTAURI team - Centre Inria d‘Universite Cĉte d’ Azur

ISBN: (纸本)9781665476881

Robots are now widely used around humans, in homes and public places like the museums, all due to their many benefits. These autonomous robots are called social or service robots and they always find it difficult to navigate in crowded environments; largely because of the high level of uncertainty in observing and predicting human behaviours in a highly dynamic environment. Uncertainty is propagated during prediction and might grow to levels that renders the whole environment unsafe for the robot leading to the so-called Freezing Robot Problem - FRP. This work presents our proposed approach to proactively account for various uncertainties during the robot's motion planning using a stochastic Nonlinear Model Predictive Controller (SNMPC). Additionally, using numerical optimization methods enables the planner to compute new control commands in realtime.

关键词： Uncertainty Navigation Service robots Computational modeling Dynamics Stochastic processes Predictive models

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Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization 27th

Delving into Feature Space: Improving Adversarial Robustness...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Cheng, Zhen Zhu, Fei Zhang, Xu-Yao Liu, Cheng-Lin State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing China School of Artificial Intelligence UCAS Beijing China Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong

ISBN: (纸本)9783031783944

The study of adversarial examples in deep neural networks has attracted great attention. Numerous methods improve adversarial robustness via shrinking the gap of features between natural examples and adversarial examples. Nevertheless, the role of individual features in adversarial robustness has not been explored adequately. In this paper, we delve into this problem from the perspective of spectral analysis in feature space. We find that while standardly trained deep models have features distributed dominantly along eigenvectors with large eigenvalues, eigenvectors with smaller eigenvalues are more sensitive to adversarial attacks. We attribute this phenomenon to the dominance of the top eigenvalues, linked to the concept of intrinsic dimensionality. The extracted features possess a small intrinsic dimensionality, enhancing generalization but resulting in the model overlooking diverse features. We propose a method called Feature Spectral Regularization (FSR) to penalize the largest eigenvalue, so as to spread the distribution of eigenvalues. Comprehensive experiments demonstrate that FSR is effective to alleviate the dominance of larger eigenvalues, increase the intrinsic dimensionality, and improve adversarial robustness on multiple datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Target-targeted Domain Adaptation for Unsupervised Semantic Segmentation

Target-targeted Domain Adaptation for Unsupervised Semantic ...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhang, Xiaohong Zhang, Haofeng Lu, Jianfeng Shao, Ling Yang, Jingyu Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9781728190778

Semantic segmentation has attracted increasing attention due to its important role in self-driving, and it is often realized by supervised learning with large number of well labeled maps. However, the labeled images are hard to be obtained in most circumstances, and the common way for unsupervised semantic segmentation is usually implemented by transferring the knowledge from source supervised domain to target unsupervised domain. Most researches focus on encouraging target predictions to be closer to the source ones through a weight-sharing network, and achieve certain performance. However, these methods often suffer from the domain shift problem that the networks are often trained towards the source domain and lead to performance degradation. In this paper, we propose a target-targeted domain adaptation approach by focusing the training on target domain. Our model consists of two components: the Image-to-image Translation (IIT) module to translate the source image to target domain and the Target-targeted Segmentation Adaptation (TSA) module to focus the semantic segmentation on target domain. The IIT module deals with image space alignment while the TSA module bridges the domain gap at the segmentation map level. In addition, we design a closed-loop learning to promote each other by employing feedback from TSA to IIT. Extensive experiments on GTA5 and SYNTHIA to Cityscapes demonstrate the effectiveness of our method in domain adaptation of unsupervised semantic segmentation.

关键词： Training Degradation Bridges Image segmentation automation conferences Semantics

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Effective Searching for the Honeybee Queen in a Living Colony

Effective Searching for the Honeybee Queen in a Living Colon...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Jan Blaha Jan Mikula Tomáš Vintr Jiří Janota Jiří Ulrich Tomáš Rouček Fatemeh Rekabi-Bana Laurenz Alexander Fedotoff Martin Stefanec Thomas Schmickl Farshad Arvin Miroslav Kulich Tomáš Krajník Faculty of Electrical Engineering Czech Technical University in Prague Czechia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czechia Computer Science Department Durham University United Kingdom Department of Zoology Artificial Life Lab Institute of Biology University of Graz Graz Austria

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Despite the importance of honeybees as pollinators for the entire ecosystem and their recent decline threatening agricultural production, the dynamics of the living colony are not well understood. In our EU H2020 RoboRoyale project, we aim to support the pollination activity of the honeybees through robots interacting with the core element of the honeybee colony, the honeybee queen. In order to achieve that, we need to understand how the honeybee queen behaves and interacts with the surrounding worker bees. To gather the necessary data, we observe the queen with a moving camera, and occasionally, we instruct the system to perform selective observations elsewhere. In this paper, we deal with the problem of searching for the honeybee queen inside a living colony. We demonstrate that combining spatio-temporal models of queen presence with efficient search methods significantly decreases the time required to find her. This will minimize the chance of missing interesting data on the infrequent behaviors or queen-worker interactions, leading to a better understanding of the queen’s behavior over time. Moreover, a faster search for the queen allows the robot to leave her more frequently and gather more data in other areas of the honeybee colony.

关键词： Computer aided software engineering automation Biological system modeling Ecosystems Robot vision systems Production Search problems Cameras Path planning Robots

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Application of Numerical methods for Traffic Flow Control

Application of Numerical Methods for Traffic Flow Control

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international conference on Control Systems, Mathematical Modelling, automation and Energy Efficiency (SUMMA)

作者： Svetlana Zhikhoreva Anatoly Pogodaev Department of Applied Mathematics Lipetsk State Technical University Lipetsk Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

Traffic control is one of the most important tasks for modern cities, where traffic intensity is constantly increasing due to population growth, urbanization and an increase in the number of vehicles. The inability to effectively organize traffic leads to serious consequences such as traffic jams, long delays, increased travel time, increased air pollution and a deterioration in the quality of life of citizens. These problems are especially acute in large metropolitan areas, where population density and transport load on infrastructure significantly exceed the average.

关键词： Adaptation models Flow production systems Urban areas Machine learning Traffic control Mathematical models Energy efficiency Numerical models Population density Optimization

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Automatic Configuration of Multi-Agent Model Predictive Controllers based on Semantic Graph World models

Automatic Configuration of Multi-Agent Model Predictive Cont...

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IEEE international conference on robotics and automation (ICRA)

作者： K. de Vos E. Torta H. Bruyninckx C. A. López Martínez M. J. G. van de Molengraft Department of Mechanical Engineering Eindhoven University of Technology The Netherlands Department of Mechanical Engineering KU Leuven Belgium Flanders Make Leuven Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We propose a shared semantic map architecture to construct and configure Model Predictive Controllers (MPC) dynamically, that solve navigation problems for multiple robotic agents sharing parts of the same environment. The navigation task is represented as a sequence of semantically labeled areas in the map, that must be traversed sequentially, i.e. a route. Each semantic label represents one or more constraints on the robots’ motion behaviour in that area. The advantages of this approach are: (i) an MPC-based motion controller in each individual robot can be (re-)configured, at runtime, with the locally and temporally relevant parameters; (ii) the application can influence, also at runtime, the navigation behaviour of the robots, just by adapting the semantic labels; and (iii) the robots can reason about their need for coordination, through analyzing over which horizon in time and space their routes overlap. The paper provides simulations of various representative situations, showing that the approach of runtime configuration of the MPC drastically decreases computation time, while retaining task execution performance similar to an approach in which each robot always includes all other robots in its MPC computations.

关键词： Runtime Navigation Processor scheduling Robot kinematics Computational modeling Heuristic algorithms Semantics

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Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework

Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinfo...

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international conference on Autonomous Robots and Agents, ICARA

作者： Xibo Li Shruti Patel Christof Büskens Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany Department for Autonomous Systems TOPAS Industriemathematik Innovation gGmbH Bremen Germany

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Deep reinforcement learning (DRL) allows a system to interact with its environment and take actions by training an efficient policy that maximizes self-defined rewards. In autonomous driving, it can be used as a strategy for high-level decision making, whereas low-level algorithms such as the hybrid A* path planning have proven their ability to solve the local trajectory planning problem. In this work, we combine these two methods where the DRL makes high-level decisions such as lane change commands. After obtaining the lane change command, the hybrid A* planner is able to generate a collision-free trajectory to be executed by a model predictive controller (MPC). In addition, the DRL algorithm is able to keep the lane change command consistent within a chosen time-period. Traffic rules are implemented using linear temporal logic (LTL), which is then utilized as a reward function in DRL. Furthermore, we validate the proposed method on a real system to demonstrate its feasibility from simulation to implementation on real hardware.

关键词： Training Trajectory planning Predictive models Deep reinforcement learning Prediction algorithms Planning Trajectory Logic Autonomous vehicles Robots

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