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Computer Engineering and intelligent control (ICCEIC) International Conference on

作者： Maolin Chen Songhang Chen Mingchang Lian School of Advanced Manufacturing Fuzhou University Quanzhou China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou China Fujian College University of Chinese Academy of Sciences Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

With the widespread application of Automated Guided Vehicles (AGVs) in warehousing and logistics systems, the optimization of multi-AGV path planning has become a critical issue. Current methods primarily focus on minimizing operating time and energy consumption but often overlook spatiotemporal distances between paths, leading to frequent path conflicts and interferences in the system. This paper proposes a neural network based time window estimation model, designed to generate time windows that better align with dynamic environments. Based on this model, we introduce a collaborative multi-AGV path planning method that optimizes spatiotemporal distances between paths. Compared to the Conflict-Based Search (CBS) method, this approach significantly improves the spatiotemporal distance between AGVs during task execution, enhancing the robustness of AGV clusters while only marginally increasing time costs.

关键词： Energy consumption Costs Remotely guided vehicles Neural networks Warehousing Estimation Collaboration Path planning Spatiotemporal phenomena Vehicle dynamics

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Hybrid Electromagnetic Modelling of Tubular Permanent Magnet Linear Motors Based on Transfer Learning Physics-Informed Neural Networks

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IET Electric Power Applications 2025年第1期19卷

作者： Jiale Guo Tao Wu Xinmei Wang Xiongbo Wan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Due to the inherent nonlinearity and saturation in the magnetic circuits of tubular permanent magnet linear motors, the analytical method (AM), while computationally efficient, often fails to capture complex electromagnetic behaviours accurately. In contrast, the finite element analysis (FEA) offers high precision but is time consuming. The nonlinearity of magnetic materials introduces strong input–output coupling, while saturation leads to localised deviations in field distributions, both of which reduce the effectiveness and generalisability of conventional modelling approaches. To overcome these challenges, a physics-informed, data-driven modelling approach is proposed. Initially, a novel hybrid modelling framework based on physics-informed neural networks (PINNs) is introduced. In this framework, AM is incorporated into both the input-output layers and the relevant variables, thereby enabling the direct embedding of physical constraints into the loss function. Consequently, the network's training process is rigorously guided in accordance with established physical principles. To further enhance prediction accuracy and generalisation, a transfer learning framework is integrated into PINN, utilising pre-trained datasets from AM and fine-tuning the model using high-accuracy datasets derived from FEA. Additionally, to optimise the physical information-related hyperparameters that impact model accuracy, functional analysis of variance is employed to quantitatively assess their importance and determine the optimal hyperparameter values. Experimental results show that, with training sample sizes representing only 5% of the FEA data, TL-PINN achieves significant improvements over DNN, including a 74.25% reduction in (1 − R 2 ), a 49.51% reduction in RMSE, and a 50.46% reduction in MAE. These findings demonstrate that TL-PINN delivers superior accuracy while utilising substantially fewer FEA datasets.

关键词： design learning (artificial intelligence) neural nets optimisation permanent magnet motors

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3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion

arXiv

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

3D scene flow characterizes how the points at the current time flow to the next time in the 3D Euclidean space, which possesses the capacity to infer autonomously the non-rigid motion of all objects in the scene. The previous methods for estimating scene flow from images have limitations, which split the holistic nature of 3D scene flow by estimating optical flow and disparity separately. Learning 3D scene flow from point clouds also faces the difficulties of the gap between synthesized and real data and the sparsity of LiDAR point clouds. In this paper, the generated dense depth map is utilized to obtain explicit 3D coordinates, which achieves direct learning of 3D scene flow from 2D images. The stability of the predicted scene flow is improved by introducing the dense nature of 2D pixels into the 3D space. Outliers in the generated 3D point cloud are removed by statistical methods to weaken the impact of noisy points on the 3D scene flow estimation task. Disparity consistency loss is proposed to achieve more effective unsupervised learning of 3D scene flow. The proposed method of self-supervised learning of 3D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds. The comparisons of multiple scene flow metrics are shown to demonstrate the effectiveness and superiority of introducing pseudo-LiDAR point cloud to scene flow estimation. © 2022, CC BY.

关键词： Three dimensional computer graphics

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Detection of Weak Signal Amplitude of Winding Deformation Based on Duffing Oscillator

Detection of Weak Signal Amplitude of Winding Deformation Ba...

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第40届中国控制会议

作者： Sheng-min Li Yuan-yuan He Yue-yue Che Xu-xia Sun Academy of Automation & Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

In order to solve the problem that it is difficult to detect the weak sinusoidal response signal when the frequency response method is used to detect the transformer winding deformation fault online,this paper proposes a method to detect the amplitude of the response signal by using Duffing ***,in order to solve the influence of the target signal phase shift on the amplitude detection in the actual detection process,a method of first detecting the signal phase using a vibrator array composed of Duffing vibrators and then detecting the amplitude is ***,using this method to process the two sets of measured response signals of online detection,the detection results show that the maximum error between the detected signal amplitude and the standard signal is 5.69%,which meets the requirements of the power industry standard,which proves that the method used in this article is It is feasible in winding deformation detection.

关键词： On-line detection of winding deformation Weak signal detection Duffing vibrator

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Reference-point-based Brain Storm Optimization Algorithm for Many-Objective Optimization

Reference-point-based Brain Storm Optimization Algorithm for...

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第32届中国过程控制会议(CPCC2021)

作者： Yali Wu Xiaoxiao Quan Jinjin Qi Yulong Wang School of Automation and Information Engineering Xi'an University of Technology Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

Brain storm optimization algorithm,as a new type of swarm intelligence optimization algorithm,has attracted the attention of many researchers as soon as it was put ***,it has been applied to various many-objective,multi-modal and practical engineering *** this paper,a brain storm optimization algorithm based on reference points was proposed to solve many-objective optimization ***,a series of reference points are introduced into the target space,and the population is clustered in an adaptive clustering method centered on the reference point poles to guide the evolution of the ***,the idea based on decomposition is combined with the mechanism of brain storm optimization algorithm to solve many-objective optimization ***,a mechanism to update the external archive set based on the strategy of enhanced dominance and weighted boundary intersection is proposed to maintain the convergence and diversity of the *** simulation results on the DTLZ series of test function sets show that the proposed algorithm based on reference points is effective.

关键词： Brain Storm Optimization Algorithm Reference Point Many-objective optimization problems adaptive cluster

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Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance

arXiv

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arXiv 2022年

作者： Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China

Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point’s movement. Scene flow estimation is used in various applications such as autonomous driving fields, activity recognition, and virtual reality fields. As it is challenging to annotate scene flow with ground truth for real-world data, this leaves no real-world dataset available to provide a large amount of data with ground truth for scene flow estimation. Therefore, many works use synthesized data to pre-train their network and real-world LiDAR data to finetune. Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network. Supervised odometry provides more accurate shared cost volume for scene flow. In addition, the proposed network has mask-weighted warp layers to get a more accurate predicted point cloud. The warp operation means applying an estimated pose transformation or scene flow to a source point cloud to obtain a predicted point cloud and is the key to refining scene flow from coarse to fine. When performing warp operations, the points in different states use different weights for the pose transformation and scene flow transformation. We classify the states of points as static, dynamic, and occluded, where the static masks are used to divide static and dynamic points, and the occlusion masks are used to divide occluded points. The mask-weighted warp layer indicates that static masks and occlusion masks are used as weights when performing warp operations. Our designs are proved to be effective in ablation experiments. The experiment results show the promising prospect of an odometry-assisted unsupervised learning method for 3D scene flow in real-world data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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NeRF in Robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in Robotics and The Advance of NeRF in Robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： Robotics

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Speech Emotion Recognition Based on Deep Learning Network with Non-local Networks

Speech Emotion Recognition Based on Deep Learning Network wi...

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Chinese automation Congress (CAC)

作者： M.T. Han Z.T. Liu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Convolutional Neural Networks (CNN) based Speech Emotion Recognition (SER) has the problem of learning bias towards stronger emotions while giving less weight to relatively faint emotions. This lack of subtleness causes the misclassification of emotions that have similar arousal such as anger and joy. To solve this problem, a novel speaker-independent SER methods consist of CNN with non-local networks and attention-based Bi-directional Long Short-Term Memory (BiLSTM) is proposed. The non-local networks can capture the dependence between any two points globally to enhance the correlation of emotional features at the utterance level. The extracted trichannel Log-Mel spectrograms features include the raw of LogMel spectrums, the Log-Mel spectrums’ deltas and delta-deltas respectively. Finally long-term contextual dependencies, local information and non-local information which is obtained by nonlocal networks is contained in the extracted feature. It was tested on the Interactive Emotional Dyadic Motion Capture Database (IEMOCAP) dataset. The experimental results show that the WAR of SER is 66.85% and the UAR of SER is 61.01%. Compared with several newly proposed SER methods, the effectiveness of the proposed model is demonstrated.

关键词： Deep learning Emotion recognition Databases Speech recognition Feature extraction Data models Convolutional neural networks

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A Multi-modal Panoramic Speaker Localization Method

A Multi-modal Panoramic Speaker Localization Method

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Chinese automation Congress (CAC)

作者： Y. Z. Long Y. W. Jian Z. T. Liu School of Automation China University of Geosciences WuHan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuHan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

As a supporting technology in the field of human-computer interaction, speaker localization method has been a research hotspot in recent years. However, the existing single-mode speaker location methods cannot meet the requirements of stability and rapidity. For improve the accuracy and effective positioning range of speaker localization system, a multi-modal panoramic speaker localization method is proposed in this paper. Firstly, the coarse position of the speaker was preliminarily obtained by sound source localization. Then, according to the initial positioning of the speaker, whether the image used for face recognition need to be stitched is decided. What’s more, the position of speaker is calculated by face detection and calibration. Finally, the results of voice and image localization are fused by coordinate transformation. The proposal is tested on self-built experimental system, and the experimental results prove the effectiveness of the proposed method.

关键词： Location awareness Image resolution Image edge detection Face recognition Standardization Cameras Robustness

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Accelerometer optimization based on real-time measurement of Hole Deviation INS with While-Drilling

Accelerometer optimization based on real-time measurement of...

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Chinese automation Congress (CAC)

作者： Yuan-Qiu Liu Fang Jin Jun-Lei Song Kai-Feng Dong Wen-Qin Mo Ya-Juan Hui School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Well deviation Measurement of While-Drilling(MWD) refers to the real-time measurement of well deviation and azimuth While-Drilling is the key for navigation in automatic drilling, real-time detection of drilling direction and automatic drilling control. In this paper, we solve the problem that the accelerometer could not accurately measure gravity because of the high-speed rotation caused by the eccentric installation of INS (Inertial Navigation system) and the acceleration of vibration line in the real-time measurement of borehole deviation and azimuth angle while drilling. Firstly, the dynamic physical model of accelerometer and gyroscope under eccentric installation is built, the non-gravity error of accelerometer measurement under eccentric motion is analyzed, and the algorithm optimization is made. Secondly, a calibration scheme for the compensation coefficients of centrifugal and tangential forces is given, which enables the carrier to make eccentric pure rotational motion on the rotating platform to determine the compensation calibration coefficient matrix. Finally, the first-order complementary filter algorithm is used to fuse the gyroscope angular velocity with the corrected acceleration for attitude calculation, and the algorithm is experimented. In this study, MPU6050 is selected as the chip of INS, and the attitude algorithm is realized by STM32. The experimental results show that the high dynamic error of the accelerometer measurement caused by eccentric motion is effectively reduced by the algorithm compensation.

关键词： Accelerometers Drilling Semiconductor device measurement Heuristic algorithms Measurement uncertainty Vibration measurement Real-time systems

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