Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053873
Xinfan Yin, Hongxu Ma, Honglei An, Liang Wang
In this paper, the X-Cell 60 SE miniature unmanned helicopter is taken as the research object. Firstly, its full nonlinear flight dynamics model is established by Newton Euler method. On this basis, the model is trimmed for the basic state of hovering, and the control quantity and state quantity in hovering state are obtained. Then, the nonlinear model is analytically linearized according to the trimmed conditions of the hovering state, and the linearized simulation model in the hovering state is obtained. The active disturbance rejection controller is designed for the attitude control loop, and the numerical simulation is carried out in Matlab/Simulink simulation environment. The simulation results show that the designed active disturbance rejection controller can effectively stabilize the attitude motion of miniature unmanned helicopter and achieve reliable attitude stabilization control, and the control performance is significantly better than $Hinfty$ robust controller, which can meet the attitude control requirements of miniature unmanned helicopter.
{"title":"Research on Attitude Active Disturbance Rejection Control for Miniature Unmanned Helicopter","authors":"Xinfan Yin, Hongxu Ma, Honglei An, Liang Wang","doi":"10.1109/ICCR55715.2022.10053873","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053873","url":null,"abstract":"In this paper, the X-Cell 60 SE miniature unmanned helicopter is taken as the research object. Firstly, its full nonlinear flight dynamics model is established by Newton Euler method. On this basis, the model is trimmed for the basic state of hovering, and the control quantity and state quantity in hovering state are obtained. Then, the nonlinear model is analytically linearized according to the trimmed conditions of the hovering state, and the linearized simulation model in the hovering state is obtained. The active disturbance rejection controller is designed for the attitude control loop, and the numerical simulation is carried out in Matlab/Simulink simulation environment. The simulation results show that the designed active disturbance rejection controller can effectively stabilize the attitude motion of miniature unmanned helicopter and achieve reliable attitude stabilization control, and the control performance is significantly better than $Hinfty$ robust controller, which can meet the attitude control requirements of miniature unmanned helicopter.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123433606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053916
Yifeng Zhu, Bo Yang, Lin Xu
In order to meet the dynamic and stable performance of the braking process in the electromechanical braking system, the relationship between the current threshold and the clamping force is selected to identify the critical point in the braking process, and the staged closed-loop control strategy is designed accordingly. At the same time, for the pressure loop of clamping force control in the braking process, the control strategy adopts fuzzy PID control. Finally, the electronic mechanical brake system simulation platform is built in the offline environment of MATLAB/Simulink. The simulation results prove that the control strategy in this paper can accurately identify the critical point in the braking process and stably follow the control objectives of each stage. Compared with the PID control, the fuzzy PID control algorithm designed in this paper improves the response speed and control quality of the target braking force within a certain range for the pressure loop which directly affects the clamping force control effect.
{"title":"Research on Multi-stage Closed-loop Control Strategy Based on Electromechanical Brake System","authors":"Yifeng Zhu, Bo Yang, Lin Xu","doi":"10.1109/ICCR55715.2022.10053916","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053916","url":null,"abstract":"In order to meet the dynamic and stable performance of the braking process in the electromechanical braking system, the relationship between the current threshold and the clamping force is selected to identify the critical point in the braking process, and the staged closed-loop control strategy is designed accordingly. At the same time, for the pressure loop of clamping force control in the braking process, the control strategy adopts fuzzy PID control. Finally, the electronic mechanical brake system simulation platform is built in the offline environment of MATLAB/Simulink. The simulation results prove that the control strategy in this paper can accurately identify the critical point in the braking process and stably follow the control objectives of each stage. Compared with the PID control, the fuzzy PID control algorithm designed in this paper improves the response speed and control quality of the target braking force within a certain range for the pressure loop which directly affects the clamping force control effect.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053910
Hongbin Ma, Yi Xu, Jie Liu
The welding technology is widely used in many manufacturing industries. The welding seams are weak targets due to unstable welding quality and the various kinds of noises. This article proposed a new method of weak-target recognition focus on welding seams, which helps to automatically track the welding seam in complex enviroment. We first relabel the data with “outer boxes” and “slopes” on the welding seam. Then we design a new recognition framework based on prior knowledge and YOLOv5 recognition algorithm. We trained weld images in many kinds of enviroment and compared the results. It shown that our method exceed the tradional method on both precision and recall.
{"title":"Weak Weld-target Recognition Based on Prior Knowledge","authors":"Hongbin Ma, Yi Xu, Jie Liu","doi":"10.1109/ICCR55715.2022.10053910","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053910","url":null,"abstract":"The welding technology is widely used in many manufacturing industries. The welding seams are weak targets due to unstable welding quality and the various kinds of noises. This article proposed a new method of weak-target recognition focus on welding seams, which helps to automatically track the welding seam in complex enviroment. We first relabel the data with “outer boxes” and “slopes” on the welding seam. Then we design a new recognition framework based on prior knowledge and YOLOv5 recognition algorithm. We trained weld images in many kinds of enviroment and compared the results. It shown that our method exceed the tradional method on both precision and recall.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127562697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053915
Mingwei Liu, Lu Liu, Lichuan Zhang, Guang Pan, Peidong Dang, Yi Chen
With the increasing stealth performance of underwater detection targets and more complex detection tasks, a single underwater unmanned underwater vehicle (UUV) has been already unable to meet the current mission requirements. Therefore, in recent years, multi-UUV cooperative detection has been widely used in certain task. To address the problem that the change of multiple UUV formations in the detection task will have a large impact on the detection effectiveness, a multi-UUV formation optimization model is proposed. Then, the formation optimization problem of multi-UUV cooperative detection is solved through the ant colony algorithm. The results show that the algorithm in this paper is more robust than the traditional algorithm. and the ability to globally search for optimal solutions.
{"title":"Multi-UUV Detection Array Optimization Based on Ant Colony Algorithm","authors":"Mingwei Liu, Lu Liu, Lichuan Zhang, Guang Pan, Peidong Dang, Yi Chen","doi":"10.1109/ICCR55715.2022.10053915","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053915","url":null,"abstract":"With the increasing stealth performance of underwater detection targets and more complex detection tasks, a single underwater unmanned underwater vehicle (UUV) has been already unable to meet the current mission requirements. Therefore, in recent years, multi-UUV cooperative detection has been widely used in certain task. To address the problem that the change of multiple UUV formations in the detection task will have a large impact on the detection effectiveness, a multi-UUV formation optimization model is proposed. Then, the formation optimization problem of multi-UUV cooperative detection is solved through the ant colony algorithm. The results show that the algorithm in this paper is more robust than the traditional algorithm. and the ability to globally search for optimal solutions.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122313567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053918
W. Andy, Wen-Yu Cheng Marty, Zhengbin Ni, Xiangnan Zhong
This paper focuses on the design of an automated statistical evaluation framework for mapping generation of Rapidly-Exploring Random Tree (RRT) frontier detectors. By evaluating the run time and distance traveled of the simulated Kobuki robot agent in a Gazebo environment, the designed framework can automatically evaluate the process on a user-defined Gazebo map for a large number of repeated simulations. We also expanded the experiment platform into customized maps with complex layouts and trial schemes. The key formulas and parameters are provided with different trial settings. During the development of this framework, we have added functions that allow the user to choose among the maps we have designed, and the initial positions of the simulated robots for each map at the beginning of each trial. We have also modified the modules developed by Umari et al. so that the RRT frontier detection process can be started automatically with pre-defined exploration area in place. Modules have also been added so that the run time and distance traveled by the simulated robot for each trial can be measured and saved to the respective CSV files for further statistical analysis. We have created additional procedures that ensure the consistency of each trial. The results show that our designed automated evaluation framework is reliable and suitable for use as a fully automated research platform for robot exploration.
{"title":"An Automated Statistical Evaluation Framework of Rapidly-Exploring Random Tree Frontier Detector for Indoor Space Exploration","authors":"W. Andy, Wen-Yu Cheng Marty, Zhengbin Ni, Xiangnan Zhong","doi":"10.1109/ICCR55715.2022.10053918","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053918","url":null,"abstract":"This paper focuses on the design of an automated statistical evaluation framework for mapping generation of Rapidly-Exploring Random Tree (RRT) frontier detectors. By evaluating the run time and distance traveled of the simulated Kobuki robot agent in a Gazebo environment, the designed framework can automatically evaluate the process on a user-defined Gazebo map for a large number of repeated simulations. We also expanded the experiment platform into customized maps with complex layouts and trial schemes. The key formulas and parameters are provided with different trial settings. During the development of this framework, we have added functions that allow the user to choose among the maps we have designed, and the initial positions of the simulated robots for each map at the beginning of each trial. We have also modified the modules developed by Umari et al. so that the RRT frontier detection process can be started automatically with pre-defined exploration area in place. Modules have also been added so that the run time and distance traveled by the simulated robot for each trial can be measured and saved to the respective CSV files for further statistical analysis. We have created additional procedures that ensure the consistency of each trial. The results show that our designed automated evaluation framework is reliable and suitable for use as a fully automated research platform for robot exploration.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128097485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053867
Xin Song, Huili Cao, Haitao You
The deep learning technology based on convolutional neural network in the field of automatic driving makes automatic driving have extremely high practical application value and scientific research value. With the continuous improvement of network real-time and edge computing, autonomous driving technology will continue to show its powerful strength to liberate people's hands in the process of driving. Therefore, controlling the direction and angle of the steering wheel rotation according to the picture captured by the camera in front of the car is the core problem needs to be solved by automatic driving. We design and implement a steering wheel rotation angle prediction system for autonomous driving. By collecting road information on the simulator, data balance analysis is carried out on the collected road picture information. We designed related algorithms such as horizontal flipping of data balance processing, angle processing of bilaterally collected pictures, and random elimination of 0-angle information. For scenarios such as the correlation conditions in the prediction process of the actual vehicle rotation angle, we have done a complete and scientific data augmentation experiment, and trained an excellent prediction network model. From the results of the road test, it can make excellent performance in multi-scenario and complex road conditions, and can more accurately predict the trajectory and direction of the vehicle to be driven. It laies a certain theoretical foundation and accumulated practical experience for the development of autonomous vehicles under smart transportation.
{"title":"Steering Wheel Rotation Angle Prediction Based on VGG-16 and Data Augmentation","authors":"Xin Song, Huili Cao, Haitao You","doi":"10.1109/ICCR55715.2022.10053867","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053867","url":null,"abstract":"The deep learning technology based on convolutional neural network in the field of automatic driving makes automatic driving have extremely high practical application value and scientific research value. With the continuous improvement of network real-time and edge computing, autonomous driving technology will continue to show its powerful strength to liberate people's hands in the process of driving. Therefore, controlling the direction and angle of the steering wheel rotation according to the picture captured by the camera in front of the car is the core problem needs to be solved by automatic driving. We design and implement a steering wheel rotation angle prediction system for autonomous driving. By collecting road information on the simulator, data balance analysis is carried out on the collected road picture information. We designed related algorithms such as horizontal flipping of data balance processing, angle processing of bilaterally collected pictures, and random elimination of 0-angle information. For scenarios such as the correlation conditions in the prediction process of the actual vehicle rotation angle, we have done a complete and scientific data augmentation experiment, and trained an excellent prediction network model. From the results of the road test, it can make excellent performance in multi-scenario and complex road conditions, and can more accurately predict the trajectory and direction of the vehicle to be driven. It laies a certain theoretical foundation and accumulated practical experience for the development of autonomous vehicles under smart transportation.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129278898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053851
Xuxiang Feng, An Li, Hongqun Zhang, Shengpu Shi
Estimating the probability distribution of an image is the key issue in lossless image compression. Though image compression can benefit from both global and local information, few works have been proposed to utilize both in lossless image compression. In this work, we propose to use a neural network for multiscale feature learning, the learned features are used to estimate the distribution of the image in a chain rule. In a further step, we utilize a context model to learn local features from the image. Finally, we combine the multiscale features with local features for image distribution learning. Our work surpasses state-of-the-art learning algorithms and several traditional codecs in several challenging datasets.
{"title":"Lossless Image Compression with Learned Local and Global Features","authors":"Xuxiang Feng, An Li, Hongqun Zhang, Shengpu Shi","doi":"10.1109/ICCR55715.2022.10053851","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053851","url":null,"abstract":"Estimating the probability distribution of an image is the key issue in lossless image compression. Though image compression can benefit from both global and local information, few works have been proposed to utilize both in lossless image compression. In this work, we propose to use a neural network for multiscale feature learning, the learned features are used to estimate the distribution of the image in a chain rule. In a further step, we utilize a context model to learn local features from the image. Finally, we combine the multiscale features with local features for image distribution learning. Our work surpasses state-of-the-art learning algorithms and several traditional codecs in several challenging datasets.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122047997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053901
Jianlei Zhang, Haiyang Gao, He Zhong, Mingwei Tang, Yujun Guo, Song Xiao, Guoqiang Gao
With the increase of the operation time of urban rail transit system, the insulation between the track and the earth is partially damaged due to environmental, stress damage and other factors, resulting in increased stray current leakage. The stray current flowing into the ground will flow into the neutral point of the transformer in the surrounding substation, which will have an impact on the urban power grid substation. The simulation model is used to analyze the impact of the location and number of rail ground insulation damage points on the ground potential and the impact of voltage and current density between substations with electrical connection relationship after rail ground insulation damage, providing support for rail ground insulation damage protection.
{"title":"The Influence Analysis of Stray Current on Urban Power Grid Substation Underlocal Damage of Rail Ground Insulation","authors":"Jianlei Zhang, Haiyang Gao, He Zhong, Mingwei Tang, Yujun Guo, Song Xiao, Guoqiang Gao","doi":"10.1109/ICCR55715.2022.10053901","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053901","url":null,"abstract":"With the increase of the operation time of urban rail transit system, the insulation between the track and the earth is partially damaged due to environmental, stress damage and other factors, resulting in increased stray current leakage. The stray current flowing into the ground will flow into the neutral point of the transformer in the surrounding substation, which will have an impact on the urban power grid substation. The simulation model is used to analyze the impact of the location and number of rail ground insulation damage points on the ground potential and the impact of voltage and current density between substations with electrical connection relationship after rail ground insulation damage, providing support for rail ground insulation damage protection.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115198383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053909
Zhitong Zhang, Honglei An, Qing Wei, Hongxu Ma
Nowadays, reinforcement learning (RL) and model predictive control (MPC) are two of the most widely used methods in robotics community. Model-based MPC enable the robot with stable locomotion capabilities, while Model-free RL provide an automatic approach to learn the policy to maximization the corresponding task performance. In this work, be aiming at utilize the advantages of these two approaches, we propose a Learning-Based Model Predictive Control (LBMPC) methodology for quadruped robot which improves MPC performance by learning the upper-layer decision parameters for MPC though a Heuristic Monte-Carlo Expectation-Maximization (HMCEM) algorithm. We validate this framework with the problem of dynamic locomotion on slippery ground by learning the friction factor which be fixed in standard MPC algorithm. Simulation results show that our LBMPC succeeds in find the optimal friction factor respect to different ground, and our heuristic overcome the problem that the conventional EM algorithms is sensitive to the initial value of policy. At last, we deduce a heuristic strategy for crude but fast ground classification based on empirical data.
{"title":"Learning-Based Model Predictive Control for Quadruped Locomotion on Slippery Ground","authors":"Zhitong Zhang, Honglei An, Qing Wei, Hongxu Ma","doi":"10.1109/ICCR55715.2022.10053909","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053909","url":null,"abstract":"Nowadays, reinforcement learning (RL) and model predictive control (MPC) are two of the most widely used methods in robotics community. Model-based MPC enable the robot with stable locomotion capabilities, while Model-free RL provide an automatic approach to learn the policy to maximization the corresponding task performance. In this work, be aiming at utilize the advantages of these two approaches, we propose a Learning-Based Model Predictive Control (LBMPC) methodology for quadruped robot which improves MPC performance by learning the upper-layer decision parameters for MPC though a Heuristic Monte-Carlo Expectation-Maximization (HMCEM) algorithm. We validate this framework with the problem of dynamic locomotion on slippery ground by learning the friction factor which be fixed in standard MPC algorithm. Simulation results show that our LBMPC succeeds in find the optimal friction factor respect to different ground, and our heuristic overcome the problem that the conventional EM algorithms is sensitive to the initial value of policy. At last, we deduce a heuristic strategy for crude but fast ground classification based on empirical data.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121842190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1109/ICCR55715.2022.10053848
Zhou Yuanyuan, Shan Chunlin, Zhu Guiqing, Yao Songpu
With the continuous development of expressways' convenient and unconscious toll collection, it is crucial to improve the accuracy of highway vehicle authentication while reducing the time delay of highway vehicle charging. Based on the characteristics of multi-body and multi-stage operation status of highway vehicles, starting from the dynamic authentication of highway vehicle driving process, we propose the highway reliable driving trajectory authentication based on heterogeneous blockchain technology. It realized the real-time inspection of vehicle information and the reliable track authentication of expressway vehicles based on track information chain association.
{"title":"A Vehicle Information Authentication Method of Expressway Based on Heterogeneous Blockchain Technology","authors":"Zhou Yuanyuan, Shan Chunlin, Zhu Guiqing, Yao Songpu","doi":"10.1109/ICCR55715.2022.10053848","DOIUrl":"https://doi.org/10.1109/ICCR55715.2022.10053848","url":null,"abstract":"With the continuous development of expressways' convenient and unconscious toll collection, it is crucial to improve the accuracy of highway vehicle authentication while reducing the time delay of highway vehicle charging. Based on the characteristics of multi-body and multi-stage operation status of highway vehicles, starting from the dynamic authentication of highway vehicle driving process, we propose the highway reliable driving trajectory authentication based on heterogeneous blockchain technology. It realized the real-time inspection of vehicle information and the reliable track authentication of expressway vehicles based on track information chain association.","PeriodicalId":441511,"journal":{"name":"2022 4th International Conference on Control and Robotics (ICCR)","volume":"125 1-3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116706292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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