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检索条件"任意字段=5th International Conference on Robotics and Automation Systems, Robotics 2010"

共 1360 条记录，以下是391-400 订阅

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Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping

Learning Embeddings with Centroid Triplet Loss for Object Id...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Anas Gouda Max Schwarz Christopher Reining Sven Behnke Alice Kirchheim TU Dortmund Lamarr Institute for Machine Learning and Artificial Intelligence Autonomous Intelligent Systems - Computer Science VI & Center for Robotics University of Bonn Germany Fraunhofer IML

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Foundation models are a strong trend in deep learning and computer vision. these models serve as a base for applications as they require minor or no further fine-tuning by developers to integrate into their applications. Foundation models for zero-shot object segmentation such as Segment Anything (SAM) output segmentation masks from images without any further object information. When they are followed in a pipeline by an object identification model, they can perform object detection without training. Here, we focus on training such an object identification model. A crucial practical aspect for an object identification model is to be flexible in input size (number of input images). As object identification is an image retrieval problem, a suitable method should handle multi-query multigallery situations without constraining the number of input images (e.g. by having fixed-size aggregation layers). the key solution to train such a model is the centroid triplet loss (CTL), which aggregates image features to their centroids. CTL yields high accuracy, avoids misleading training signals and keeps the model input size flexible. In our experiments, we establish a new state of the art on the ArmBench object identification task, which shows general applicability of our model. We furthermore demonstrate an integrated unseen object detection pipeline on the challenging HOPE dataset, which requires finegrained detection. there, our pipeline matches and surpasses related methods which have been trained on dataset-specific data. Code and pretrained models are available. 5

关键词： Training Location awareness Image segmentation Accuracy Pipelines Object detection Object segmentation Market research Object recognition Proposals

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Spatial Pooling Network for Lane Line Segmentation 5

Spatial Pooling Network for Lane Line Segmentation

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5th international conference on automation, Control and robotics Engineering, CACRE 2020

作者： Ge, Yanteng Chen, Yang-Yang Zhang, Faxiang Southeast University School of Automation Nanjing China

ISBN: (纸本)9781728198880

In this paper, a novel method named spatial pooling is proposed for visual lane line segmentation. Occlusion is one of the most common and difficult scenes among various and complex road environments. In most existing references, the structure of lane line is still not considered for the occlusion problem. To better solve this case and enhance the long-distance information fusion for lane lines, a spatial pooling module is proposed firstly by using a combination of horizontal and vertical pooling to achieve pooling in the direction across the lane line. this method can enhance the information fusion ability in oblique directions and improve the lane line detection performance. then, for further study, we propose an end-to-end network for lane line segmentation based on the spatial pooling module. Finally, the experimental results on tuSimple lane line dataset verify the effectiveness of our method. © 2020 IEEE.

关键词： Image segmentation

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Solving discrete time-varying complex division using recurrent neural network with disturbance suppression 26

Solving discrete time-varying complex division using recurre...

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26th international conference on automation and Computing, ICAC 2021

作者： Shi, Yang Zhao, Wenhan Wang, Jie Li, Jian Gerontitis, Dimitrios K. Yangzhou University School of Information Engineering Yangzhou225127 China Xinyang Normal University School of Computer and Information Technology Xinyang464000 China International Hellenic University Department of Information and Electronic Engineering Thessaloniki Greece

ISBN: (纸本)9781860435577

Discrete time-varying complex division has perplexed researchers in many fields for many years. How to solve such problem has actually been discussed for a long time. In recent years, lots of researchers have studied the recurrent neural network (RNN) model for solving time-varying problems. As an extended mathematical problem, we introduce the discrete time-varying complex division from the traditional division and reducing disturbance by using the 5-instant discretization formula type advanced discrete time-varying recurrent neural network (5IDF-type advanced DTRNN) model. First, the continuous time-varying complex division is proposed based on the traditional Euclid division. then, inspired by achievements of previous researches, we equivalently transform the continuous time-varying complex division into a simple matrix-vector equation in the continuous time-varying environment. Accordingly, the discrete time-varying complex division is transformed into the discrete time-varying matrix vector equation. In addition, we also show the 5-instant discretization formula and propose the 5IDF-type advanced DTRNN model. Numerical experiments further prove that validity and superiority of such 5IDF-type advanced DTRNN model. © 2021 Chinese automation and Computing Society in the UK-CACSUK.

关键词： Recurrent neural networks

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Proceedings - 2018 4th international conference on Control, automation and robotics, ICCAR 2018

Proceedings - 2018 4th International Conference on Control, ...

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4th international conference on Control, automation and robotics, ICCAR 2018

ISBN: (纸本)9781538663387

the proceedings contain 94 papers. the topics discussed include: safe human robot collaboration - operation area segmentation for dynamic adjustable distance monitoring;genetic algorithms and unsupervised machine learning for predicting robotic manipulation failures for force-sensitive tasks;multi-robotic arms automated production line;NURBS based trajectory generation for an industrial five axis needle winding robot;analysis of using an active artificial spine in a quadruped robot;parameters tuning of snake robots sidewinding gait using Bayesian optimization;data buffering in mobile robot control systems;ROS based service robot platform;comprehensive evaluation of the performance of rescue robots using victim robots;static force, torque and structural analysis of 5R robotic arm - simulation and practical implementation;and development of a 3D-printed biped robot with distributed joint control.

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Performance degradation prediction of PEMFC based on adaptive variational mode decomposition and deep belief network 2021

Performance degradation prediction of PEMFC based on adaptiv...

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5th international conference on robotics, Control and automation, ICRCA 2021

作者： Xie, Yucen Zou, Jianxiao Peng, Chao Zhu, Yun School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)9781450387484

Proton exchange membrane fuel cell (PEMFC) degradation prediction is essential, especially in the industrial power generation system, since it can provide decision supports for equipment maintenance. As degradation phenomena inside PEMFC are strongly nonlinear and inter-coupled, accurate degradation prediction is extremely challenging. thus, a novel data-driven prediction method based on the adaptive variational mode decomposition (AVMD) and deep belief network (DBN) is proposed. Firstly, an improved AVMD method is employed to decomposes the original voltage series into multiple sub-signals, which helps to analyze the different degradation characteristics of PEMFC. then, the sample entropy (SE) theory is employed to filter out the noise sub-signals and reconstruct the rest sub-signals to strengthen significant features. Finally, the predictions are performed for each reconstructed sub-signal by the DBN method that has powerful mining ability for latent degradation features. the overall prediction results of the AVMD-DBN method are in good agreement with real experimental data, which indicates that the proposed method has good practicality for the health management of the PEMFC power generation system in industrial applications. © 2021 ACM.

关键词： Variational mode decomposition

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Precise Torque-output Estimation of a Reaction-force-sensing Series Elastic Actuator for Human-robot Interaction 9

Precise Torque-output Estimation of a Reaction-force-sensing...

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9th IFAC Symposium on Mechatronic systems (MECHATRONICS) / 16th international conference on Motion and Vibration Control (MoViC)

作者： Choi, Sanguk Lee, Sumin Kong, Kyoungchul Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

A series elastic actuator (SEA) is mainly used in human-robot interaction applications. Especially, a reaction-force-sensing SEA (RFSEA), where the spring is located between the ground and the actuator, has been developed as a practical implementation of the SEAs, as their form-factors are superior to the conventional SEAs. However, the RFSEA has limitations on its torque control performance. the output torque of an RFSEA is estimated as the spring torque, assuming the load is fixed. However, since the load moves in human-robot interaction applications, the assumption causes significant degradation in the control performance of the RFSEA. this paper presents a precise torque estimation method for the RFSEA. the dynamics of the RFSEA were analyzed, modeling the actuator as a two-mass system. the output torque was verified as the function of two variables from the dynamic analysis: the stator angle and the angular velocity of the rotor. In this paper, the torque estimator with a single encoder is proposed to maintain the form-factor of the RFSEA. An encoder measures the stator angle, and a designed observer based on a disturbance observer estimates the angular velocity of the rotor. the performance of the proposed torque estimator was verified experimentally. From the experimental results, an error of the proposed torque estimator was reduced by 37% compared to the conventional when the load moves by mimicking the human movement. Copyright (c) 2022 the Authors. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Torque Estimation Reaction-force-sensing Series Elastic Actuator Human-Robot Interaction Human-Machine systems and robotics and automation

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Time-Optimal Trajectory Planning Based on Dynamics for Industrial Robot 5

Time-Optimal Trajectory Planning Based on Dynamics for Indus...

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5th international conference on automation, Control and robotics Engineering, CACRE 2020

作者： Wang, Rundong Kong, Minxiu Cheng, Yanchun Castellani, Marco Harbin Institute of Technology State Key Laboratory of Robotics and System School of Mechatronics Engineering Harbin China University of Birmingham Department of Mechanical Engineering Birmingham United Kingdom

ISBN: (纸本)9781728198880

Given the trajectory of the industrial robot in Cartesian space, the existing methods only consider the kinematic constraints when optimizing the time trajectory planning, and ignore the maximum driving ability of the motor, which is likely to cause an overrun alarm and greatly reduce the trajectory accuracy. therefore, this paper studies the time-optimal trajectory planning algorithm based on dynamics. Firstly, the algorithm discretizes the path, calculates the Cartesian coordinates of each discrete point and the differential about the path length. then, this algorithm considers the constraints of dynamics and kinematics, uses the principle of linear programming, recursively calculates the upper limit value of speed and the maximum acceleration value at each discrete point, and calculates the speed value at each discrete point and keeps it within its allowable ranges. Finally, this algorithm uses the speed to calculate the corresponding time of each discrete point, and according to the interpolation period, outputs the path position of each period, the position and orientation in the Cartesian space and joint parameters. the MATLAB program is written to verify the feasibility and efficiency of the algorithm. © 2020 IEEE.

关键词： Industrial robots

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Path Planning of Dual Ammunition Loading Robot Based on Velocity Repulsion Field

Path Planning of Dual Ammunition Loading Robot Based on Velo...

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IEEE international conference on automation, Electronics and Electrical Engineering (AUTEEE)

作者： Qicheng Dong Zhigang Xu Junyi Wang College of Mechanical and Electrical Engineering Shenyang University of Aeronautics and Astronautics Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Institute of Robotics and Intelligent Manufacturing Innovation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781665471978

ISBN: (纸本)9781665471985

Aiming at the problems of slow charging speed of single ammunition loading robot and complicated path planning of dual robots, a path planning method of dual ammunition loading robots based on velocity repulsion field is proposed. Firstly, a robot collision detection system is established, and the robot is simplified by the form of sphere enclosing, so that the collision detection between robots can be converted into geometric distance detection; secondly, the collision-free motion path of the double-charged robot is planned by using the velocity repulsion field algorithm; finally, the experimental verification is carried out in matlab. the results show that the algorithm can effectively plan the collision-free path of the robot, which satisfies the work requirements and verifies the reliability and effectiveness of the algorithm.

关键词： Electrical engineering Loading Fixtures Programming Reliability engineering Path planning Stability analysis

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Research on water quality prediction method based on AE-LSTM 5

Research on water quality prediction method based on AE-LSTM

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5th international conference on automation, Control and robotics Engineering, CACRE 2020

作者： Zhang, Huiqing Jin, Kemei Beijing University of Technology School of Automation Department of Informatics Beijing China

ISBN: (纸本)9781728198880

Aiming at the traditional prediction methods of related parameters that affect water quality, they usually only consider the temporal characteristics of the related parameters of water quality and ignore the problem that water quality changes are multivariate related, A prediction method of spatiotemporal correlation water quality parameters based on automatic encoder (AE) dimensionality reduction and long and short time memory (LSTM) neural network is proposed. Firstly, considering that water quality parameter changes have obvious time characteristics, a time series prediction model of water quality parameters is established based on LSTM. Secondly, considering that the water quality changes have multiple correlations, the upstream water quality will also affect the downstream water quality. If all the water quality parameters of the upstream station are added to the prediction model, redundant features will reduce the accuracy of parameter prediction. therefore, the automatic encoder is used to reduce the dimensionality of the relevant parameters. Finally, the data set of Lang fang Water Quality Automatic Monitoring Station is applied to monitor the effectiveness of the method. By predicting the concentration of total phosphorus (TP) and total nitrogen (TN), the method is found to have better prediction accuracy and robustness. © 2020 IEEE.

关键词： Long short-term memory

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Inertial sensor-based movement classification with dimension reduction based on feature aggregation

Inertial sensor-based movement classification with dimension...

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international Symposium on Computational Intelligence and Informatics

作者： Peter Sarcevic József Sárosi Dominik Csík Ákos Odry Department of Mechatronics and Automation University of Szeged Szeged Hungary

ISBN: (纸本)9798350398830

In this paper, a novel feature aggregation method is proposed for dimension reduction in inertial sensor-based pattern recognition applications. the method is utilized in a movement classification algorithm, which applies measurements of wearable sensor units attached to the wrists of the subjects. the feature aggregation is realized separately for each feature type by using a linear combination of the feature values extracted for each sensor axis. the weights are computed using genetic algorithm-based optimization, which uses the classification efficiency provided by the k-nearest neighbor (kNN) method to compute the fitness value. After the weights are determined for all feature types and the aggregated inputs are computed for both the training and validation data, a multi-layer perceptron (MLP) classifier is trained, which can be used during real-time classification. the algorithm was tested with multiple datasets based on features extracted using different processing window sizes and used sensor combinations. the achieved results show that the proposed weighted aggregation provides significantly, mostly 5-6%, higher recognition rates on validation data than when equal coefficients are utilized during aggregation. the method in the case of some datasets with the same configuration gives even higher classification efficiencies than when using the features extracted for the sensor axes separately.

关键词： Dimensionality reduction Training Wrist Feature extraction Robot sensing systems Pattern recognition Classification algorithms

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