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Deep Reinforcement Learning-Based Motion Control for Unmanned Vehicles from the Perspective of multi-sensor data fusion

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第10期33卷 2450185-2450185页

作者： Wei, Hongbo Cui, Xuerong Zhang, Yucheng Chen, Haihua Zhang, Jingyao China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266000 Peoples R China Chinese Acad Sci Inst Comp Technol Ctr Adv Comp Res Beijing 100080 Peoples R China

In this paper, the vehicle position points obtained by multi-sensor fusion are taken as the observed values, and Kalman filter is combined with the vehicle kinematics equation to further improve the vehicle trajectory. To realize this, mathematical principles of deep reinforcement learning are analyzed, and the theoretical basis of reinforcement learning is also analyzed. It is proved that the controller based on dynamic model is better than the controller based on kinematics in deviation control, and the performance of the controller based on deep reinforcement learning is also verified. The simulation data show that the proportion integration differentiation (PID) controller has a better tracking effect, but it does not have the constraint ability, which leads to radical acceleration change, resulting in unstable acceleration and deceleration control. Therefore, the deep reinforcement learning controller is selected as the longitudinal velocity tracking controller. The effectiveness of lateral and longitudinal motion decoupling strategy is verified by simulation experiments.

关键词： Deep reinforcement learning motion control unmanned vehicles multi-sensor data fusion dynamic model

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An Innovative multi-sensor data fusion method based on thrice deeply-fusion architecture for multi-object tracking

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2023年第12期237卷 2679-2695页

作者： Wang, Bai-chao Liu, Cong-zhi Zhang, Li-tong Wang, Da-sen Changchun Univ Sci & Technol Sch Mech & Elect Engn Changchun Peoples R China Yanshan Univ Sch Mech Engn Qinhuangdao Peoples R China Inner Mongolia Met Mat Res Inst Baotou Peoples R China Changchun Univ Sci & Technol Sch Mech & Elect Engn 7089 Weixing Rd Changchun 130022 Peoples R China

For the purpose of track maintenance in a cluttered environment with some false alarms, false dismissals and measurement disturbances, this paper presents a new multi-object tracking procedure with uncertainty of the source of the measurement data returned from multi-sensor. The tracking method is a thrice deeply-fusion approach constructed by a primary fusion based on an improved probabilistic data association filter (IPDAF), a secondary fusion with historical motion trajectories, and a thrice fusion with road-markings. It incorporates the existence probabilities of the individual tracks and the variable number of objects based on the Bayesian estimation theory, which can improve the tracking performances effectively in an environment with high clutter density. A binary 2-D assignment is adopted for the optimal data association, which is established as a nonlinear optimization problem. In the motion modeling, it introduces multiple measurement models for different sensors into the method. Then, the estimation could be performed with greater reliability. The computational efficiency is satisfying and it can be used for real-time application, which is verified by two real test scenarios.

关键词： multi-object tracking multi-sensor data fusion Improved probabilistic data association filter Bayesian estimation theory 2-D assignment

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multi-sensor data fusion and nonlinear programming-based path prediction for escaping from engagement in combat

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ARCHIVES OF CONTROL SCIENCES 2024年第2期34卷 247-269页

作者： Gokal, Enver Nurullah Sakarya, Ufuk Turkish Aerosp Ind Istanbul Turkiye YILDIZ Tech Univ Fac Appl Sci Dept Aviat Elect & Elect Istanbul Turkiye YILDIZ Tech Univ Dept Elect & Commun Engn Fac Elect & Elect Istanbul Turkiye

One of the most important factors that bring success in modern warfare is to show air superiority. Unmanned aerial vehicles (UAVs) have now become an essential component of military air operations. UAVs can be operated in two ways: by pilots from remote control stations or by flying autonomously. Under the condition of disconnection from the control station, UAVs have trouble maintaining navigation and maneuverability. By applying multisensor data fusion, an escape path prediction algorithm was developed and presented as an engagement escape method in this study. To develop the algorithm for prediction of the optimal escape route, data from various sensors are collected and processed under the influence of noise. The data from the distance and angle sensors are interpreted in the Extended Kalman Filter and estimations are made. The instant optimal escape route is created by applying the constrained optimization method on the estimations made. The main motivation of this study is developing a deterministic-based method to get the certification of it in aviation. Therefore, instead of stochastic-based learning approaches, a deterministic approach is preferred. Nonlinear programming is used as the constraint optimization method because the constraints and objective function are nonlinear. In the selected scenarios, it can be seen in the simulation results that the proposed method shows a promising result in terms of escape from engagement.

关键词： escaping from engagement nonlinear programming multi-sensor data fusion

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multi-sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review

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MEASUREMENT 2024年 232卷

作者： Kibrete, Fasikaw Woldemichael, Dereje Engida Gebremedhen, Hailu Shimels Addis Ababa Sci & Technol Univ Coll Engn Dept Mech Engn POB 16417 Addis Ababa Ethiopia Addis Ababa Sci & Technol Univ Artificial Intelligence & Robot Ctr Excellence POB 16417 Addis Ababa Ethiopia

Rotating machines are extensively utilized in diverse industries, and their malfunctions can result in significant financial consequences and safety risks. Consequently, there has been growing research interest in the intelligent fault diagnosis of rotating machines, particularly through the utilization of multi-sensor condition monitoring data. However, a comprehensive review focusing on multi-sensor data fusion methods is lacking. To bridge this gap, this paper provides a comprehensive analysis of the existing literature on the application of multi-sensor data fusion techniques to diagnose faults in rotating machines. Basic concepts of multi-sensor data fusion are first provided, establishing a robust foundation for subsequent discussions. The review then provides an in-depth analysis of the applications of multi-sensor data fusion in intelligent diagnosis for rotating machines. Furthermore, this review paper highlights the current challenges encountered in multi-sensor data fusion for intelligent fault diagnosis of rotating machines. By considering these challenges and consolidating knowledge from various sources, this paper proposes future research directions in this field. This review article serves as a valuable resource for researchers, practitioners, and decision-makers in the domain of intelligent fault diagnosis of rotating machines. The review provides comprehensive insights into the latest advancements of multi-sensor data fusion techniques and guiding future research directions in the measurement sciences.

关键词： Condition monitoring Intelligent fault diagnosis multi-sensor data fusion Rotating machines sensor integration

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multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system

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ISA TRANSACTIONS 2021年 112卷 137-149页

作者： Mi, Xiangjun Lv, Tongxuan Tian, Ye Kan, Bingyi Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China

multi-sensor data fusion plays an irreplaceable role in actual production and application. Dempster-Shafer theory (DST) is widely used in numerous fields of information modeling and information fusion due to the flexibility and effectiveness of processing uncertain information and dealing with uncertain information without prior probabilities. However, when highly contradictory evidence is combined, it may produce results that are inconsistent with human intuition. In order to solve this problem, a hybrid method for combining belief functions based on soft likelihood functions (SLFs) and ordered weighted averaging (OWA) operators is proposed. More specifically, a soft likelihood function based on OWA operators is used to provide the possibility to fuse uncertain information compatible with each other. It can characterize the degree to which the probability information of compatible propositions in the collected evidence is affected by unknown uncertain factors. This makes the results of using the Dempster's combination rule to fuse uncertain information from multiple sources more comprehensive and credible. Experimental results manifest that this method is reliable. Example and application show that this method has obvious advantages in solving the problem of conflict evidence fusion in multi-sensor. In particular, in target recognition, when three pieces of evidence are fused, the target recognition rate is 96.92%, etc. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： multi-sensor data fusion Soft likelihood functions Ordered weighted averaging aggregation Dempster-Shafer theory Target recognition system

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Effective detection algorithm of electronic information and signal processing based on multi-sensor data fusion

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EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 2023年第3期26卷 519-526页

作者： Cao, Ting Nanyang Inst Technol Sch Informat Engn Nanyang Peoples R China

Appropriate vibration diagnostics when cutting a variety of products is critical for cutting quality and scrap and consumables reduction. Collecting and combining data from various sensors allows for realtime data collection during processing for system monitoring. The purpose of this study is to develop a method for effectively diagnosing milling machine vibrations using multi-sensor data fusion. It is proposed that sound and frequency signals be preprocessed to isolate specific vibration features, and then classified and aggregated for a complete vibration diagnostics system. After preliminary wavelet signal conversion, a deep belief network has been developed for each sensor to classify vibration features. When combining classification results, the fuzzy Choquet integral algorithm is used. Thus, a comprehensive judgment is obtained. It has been demonstrated that for neural networks, 100 epochs are sufficient for learning to take place. It has been discovered that the accuracy of classification and inference of the correct solution increases from 75% to 98% as the number of vibration feature combinations increases. When compared to the Dempster-Schafer evidence theory, the fuzzy integral fusion algorithm provides a high level of vibration detection accuracy of up to 98.7%. The resulting diagnostic procedure is simple and effective. It can be used to control the cutting process on machine tools in industrial settings. Improvements to the scheme are suggested, including the use of intelligent technologies to automate the process.

关键词： multi-sensor data fusion Vibration diagnostics Signal processing Deep belief network Fuzzy Choquet integral Dempster-Schafer evidence theory

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Train bearing fault diagnosis based on multi-sensor data fusion and dual-scale residual network

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NONLINEAR DYNAMICS 2023年第16期111卷 14901-14924页

作者： He, Deqiang Lao, Zhenpeng Jin, Zhenzhen He, Changfu Shan, Sheng Miao, Jian Guangxi Univ Sch Mech Engn Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China CRRC Zhuzhou Inst Co Ltd Zhuzhou 412001 Peoples R China

As one of the vital components of trains, the condition of train bearings is closely related to the safe operation of trains. Traditional bearing fault diagnosis methods based on single sensors are incapable of extracting feature information fully, resulting in low fault diagnostic accuracy. To solve the above problem, a fault diagnosis method for train bearings based on multi-sensor data fusion and dual-scale residual network (MSDF-DSRNet) is proposed in this paper. Firstly, a multi-sensor data fusion method is designed to extract fault feature information comprehensively. The low-dimensional features embedded in the high-dimensional nonlinear space of multi-sensor data are extracted effectively and fused into a three-dimensional pixel matrix. Secondly, a novel intelligent diagnosis method is proposed based on the dual-scale residual network. Both in-depth and shallow features are learned on two scales, and the fault-related information in different spatial dimensions is captured, which improves the extraction ability of fusion features and effectively reduces time loss. Finally, the feasibility and effectiveness of the proposed method are verified by three experiments. The accuracy of the proposed method in the train traction motor bearing dataset reaches 99.70%, 99.75%, 99.85% and 99.85%, respectively. The results show that MSDF-DSRNet performs better in comprehensive fault diagnosis than other methods.

关键词： Train bearing multi-sensor data fusion Fault diagnosis Feature extraction Residual network

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multi-sensor data fusion and Feature Extraction for Cutting Tool Condition Monitoring: A Review 10

Multi-sensor Data Fusion and Feature Extraction for Cutting ...

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IEEE 10th International Conference on Information, Communication and Networks (ICICN)

作者： Liu, Riliang Zhu, Zhiying Zeng, Yunfei Shandong Univ Sch Mech Engn Jinan Peoples R China

ISBN: (纸本)9781665490825

Cutting tools are the key component for removing material in machining process. Tool wear will definitely impair machining quality and efficiency and even result in more serious consequences. With the development of intelligent algorithms, it is necessary to reveal the relation between tool condition and the data collected in actual machining, and to develop a reasonable monitoring system to ensure that the tool be replaced appropriately under certain circumstances. Aiming at tool wear monitoring in milling process, this paper discusses the commonly used sensor data types. Then, data fusion technology is addressed, and the feature recognition and tool wear monitoring methods using machine learning are covered. Finally, an implementation of residual convolution neural network is presented.

关键词： tool wear monitoring multi-sensor data fusion deep learning feature extraction convolutional neural network

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multi-sensor data fusion method based on divergence measure and probability transformation belief factor

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APPLIED SOFT COMPUTING 2023年 145卷

作者： Hu, Zhentao Su, Yujie Hou, Wei Ren, Xing Henan Univ Sch Artificial Intelligence Zhengzhou 450046 Peoples R China

Dempster-Shafer evidence theory is widely used in multi-sensor data fusion. However, how to manage the counterintuitive result generated by the highly conflicting evidence remains an open question. To solve the problem, a novel multi-sensor data fusion method is proposed, which analyses the credibility of evidence from both the discrepancy between evidences and the factors of evidence itself. Firstly, a new Belief Kullback-Leibler divergence is put forward, which evaluates the credibility of evidence from the discrepancy between evidences. Secondly, another credibility measure called the Probability Transformation Belief Factor is defined, which assesses the credibility of evidence from the evidence itself. These two credibilities are combined as the comprehensive credibility of evidence. Furthermore, considering the uncertainty of evidence, a new belief entropy based on the cross-information within the evidence is presented, which is applied to quantify the information volume of evidence and to adjust the comprehensive credibility of evidence. The adjusted comprehensive credibility is regarded as the final weight to modify the body of evidence. Finally, the Dempster's combination rule is applied for fusion. Experiment and applications show that the proposed method is effective and superior. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Dempster-Shafer evidence theory Divergence measure Probability transformation belief factor Belief entropy multi-sensor data fusion

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multi-sensor data fusion and bidirectional-temporal attention convolutional network for remaining useful life prediction of rolling bearing

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第10期34卷 105126-105126页

作者： Liang, Haopeng Cao, Jie Zhao, Xiaoqiang Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Lanzhou Univ Technol Sch Elect & Informat Engn Lanzhou 730050 Peoples R China

Remaining useful life (RUL) prediction is crucial in the field of engineering, which can reduce the frequency of accidents and the maintenance cost of machinery. With the increasing complexity of rotating machinery, the data analysis methods based on deep learning have become the mainstream methods of prediction work. However, most of the current RUL prediction methods only use single-sensor data as input, which cannot effectively use multi-sensor data. In addition, as an advanced deep learning prediction method, temporal convolutional network (TCN) only uses the past time information of vibration data to determine the current health status of bearings, while ignoring the importance of future time information of vibration data. To solve the above problems, a bearing RUL prediction method based on multi-sensor data fusion and bidirectional-temporal attention convolutional network (Bi-TACN) is proposed in this paper. In multi-sensor data fusion, multi-sensor data are combined into multi-channel data, and a channel-weighted attention is designed to emphasize the importance of each sensor data. Compared with traditional multi-sensor data fusion, the proposed fusion method allows deep prediction networks to learn more useful feature information from multi-sensor data. Then, Bi-TACN is developed to predict the RUL of bearings. Bi-TACN is mainly composed of the forward TCN block and the backward TCN block, both of which can learn the past and future time information of multi-sensor data simultaneously. Moreover, a temporal attention mechanism is embedded in Bi-TACN to adaptively calibrate the weights of the two TCN blocks, so as to achieve dynamic feature fusion of past and future time information. RUL prediction experiments are carried out through Xi'an Jiao tong University bearing dataset and PHM 2012 bearing dataset respectively. Compared with the advanced prediction methods, the proposed method can accurately predict the RUL of more types of bearings and has low predic

关键词： rolling bearing remaining useful life prediction multi-sensor data fusion bidirectional-temporal attention convolutional network

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