Pub Date : 2023-01-28DOI: 10.1142/s2301385023020016
Bin Xin
{"title":"Editorial: Special Issue on Perception, Decision and Control of Unmanned Systems Under Complex Conditions","authors":"Bin Xin","doi":"10.1142/s2301385023020016","DOIUrl":"https://doi.org/10.1142/s2301385023020016","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128556164","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 : 2022-08-18DOI: 10.1142/s2301385023310039
Pengpeng Zhang, Tengfei Liu, Jie Chen, Zhong-Ping Jiang
{"title":"Recent Developments in Event-Triggered Control of Nonlinear Systems: An Overview","authors":"Pengpeng Zhang, Tengfei Liu, Jie Chen, Zhong-Ping Jiang","doi":"10.1142/s2301385023310039","DOIUrl":"https://doi.org/10.1142/s2301385023310039","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994574","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 : 2022-08-06DOI: 10.1142/s2301385023500152
E. H. Kapeel, Ehab Safwat, A. Kamel, M. Khalil, Y. Elhalwagy, H. Hendy
{"title":"Physical Modeling, Simulation and Validation of Small Fixed-Wing UAV","authors":"E. H. Kapeel, Ehab Safwat, A. Kamel, M. Khalil, Y. Elhalwagy, H. Hendy","doi":"10.1142/s2301385023500152","DOIUrl":"https://doi.org/10.1142/s2301385023500152","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122827326","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 : 2022-07-15DOI: 10.1142/s2301385023500139
Djamel Dhahbane, S. Sakhi, A. Nemra
{"title":"Hardware Implementation of Attitude Estimation Methods Using Multiple GPS Receivers","authors":"Djamel Dhahbane, S. Sakhi, A. Nemra","doi":"10.1142/s2301385023500139","DOIUrl":"https://doi.org/10.1142/s2301385023500139","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131769508","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 : 2022-07-07DOI: 10.1142/s2301385023410042
Liling Ma, Jian Guo, Jiehao Li, Junzheng Wang
This research provides a novel approach for detecting multi-legged robot actuator faults. The most significant concept is to design the Fault Diagnosis Generative Adversarial Network (FD-GAN) to fully adapt to the fault diagnosis problem with insufficient data. We found that it is difficult for methods based on classification and prediction to learn failure patterns without enough data. A straightforward solution is to use massive amounts of normal data to drive the diagnostic model. We introduce frequency-domain information and fuse multi-sensor data to increase the features and expand the difference between normal data and fault data. A GAN-based framework is designed to calculate the probability that the enhanced data belongs to the normal category. It uses a generator network as a feature extractor, and uses a discriminator network as a fault probability evaluator, which creates a new use of GAN in the field of fault diagnosis. Among the many learning strategies of GAN, we find that a key point that can distinguish the two types of data is to use the hidden layer noise with appropriate discrimination as the excitation. We also design a fault location method based on binary search, which greatly improves the search efficiency and engineering value of the entire method. We have conducted a lot of experiments to prove the diagnostic effectiveness of our architecture in various road conditions and working modes. We compared FD-GAN with popular diagnostic methods. The results show that our method has the highest accuracy and recall rate.
{"title":"A Noise-Excitation Generative Adversarial Network for Actuator Fault Diagnosis of Multi-legged Robot","authors":"Liling Ma, Jian Guo, Jiehao Li, Junzheng Wang","doi":"10.1142/s2301385023410042","DOIUrl":"https://doi.org/10.1142/s2301385023410042","url":null,"abstract":"This research provides a novel approach for detecting multi-legged robot actuator faults. The most significant concept is to design the Fault Diagnosis Generative Adversarial Network (FD-GAN) to fully adapt to the fault diagnosis problem with insufficient data. We found that it is difficult for methods based on classification and prediction to learn failure patterns without enough data. A straightforward solution is to use massive amounts of normal data to drive the diagnostic model. We introduce frequency-domain information and fuse multi-sensor data to increase the features and expand the difference between normal data and fault data. A GAN-based framework is designed to calculate the probability that the enhanced data belongs to the normal category. It uses a generator network as a feature extractor, and uses a discriminator network as a fault probability evaluator, which creates a new use of GAN in the field of fault diagnosis. Among the many learning strategies of GAN, we find that a key point that can distinguish the two types of data is to use the hidden layer noise with appropriate discrimination as the excitation. We also design a fault location method based on binary search, which greatly improves the search efficiency and engineering value of the entire method. We have conducted a lot of experiments to prove the diagnostic effectiveness of our architecture in various road conditions and working modes. We compared FD-GAN with popular diagnostic methods. The results show that our method has the highest accuracy and recall rate.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132456165","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}
Multi-robot simultaneous localization and mapping (MR-SLAM) is of great importance for enhancing the efficiency of large-scale environment exploration. Despite remarkable advances in schemes for cooperation, there is a critical lack of approaches to handle multiple uncertainties inherent to MR-SLAM in large-scale environments. This paper proposes a multi-uncertainty captured multi-robot lidar odometry and mapping (MUC-LOAM) framework, to quantify and utilize the uncertainties of feature points and robot mutual poses in large-scale environments. A proposed hybrid weighting strategy for pose update is integrated into MUC-LOAM to handle feature uncertainty from distance changing and dynamic objects. A devised Bayesian Neural Network (BNN) is proposed to capture mutual pose uncertainty. Then the covariance propagation of quaternions to Euler angles conversion is leveraged to filter out unreliable mutual poses. Another covariance propagation through coordinate transformations in nonlinear optimization improves the accuracy of map merging. The feasibility and enhanced robustness of the proposed framework for large-scale exploration are validated on both public datasets and real-world experiments.
{"title":"Multi-Uncertainty Captured Multi-Robot Lidar Odometry and Mapping Framework for Large-Scale Environments","authors":"Guang-ming Xiong, Junyi Ma, Huilong Yu, Jingyi Xu, Jiahui Xu","doi":"10.1142/s2301385023410030","DOIUrl":"https://doi.org/10.1142/s2301385023410030","url":null,"abstract":"Multi-robot simultaneous localization and mapping (MR-SLAM) is of great importance for enhancing the efficiency of large-scale environment exploration. Despite remarkable advances in schemes for cooperation, there is a critical lack of approaches to handle multiple uncertainties inherent to MR-SLAM in large-scale environments. This paper proposes a multi-uncertainty captured multi-robot lidar odometry and mapping (MUC-LOAM) framework, to quantify and utilize the uncertainties of feature points and robot mutual poses in large-scale environments. A proposed hybrid weighting strategy for pose update is integrated into MUC-LOAM to handle feature uncertainty from distance changing and dynamic objects. A devised Bayesian Neural Network (BNN) is proposed to capture mutual pose uncertainty. Then the covariance propagation of quaternions to Euler angles conversion is leveraged to filter out unreliable mutual poses. Another covariance propagation through coordinate transformations in nonlinear optimization improves the accuracy of map merging. The feasibility and enhanced robustness of the proposed framework for large-scale exploration are validated on both public datasets and real-world experiments.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130860746","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 : 2022-06-27DOI: 10.1142/s2301385023310027
Hao Liu, Bahare Kiumarsi, Yusuf Kartal, A. T. Koru, H. Modares, F. Lewis
{"title":"Reinforcement Learning Applications in Unmanned Vehicle Control: A Comprehensive Overview","authors":"Hao Liu, Bahare Kiumarsi, Yusuf Kartal, A. T. Koru, H. Modares, F. Lewis","doi":"10.1142/s2301385023310027","DOIUrl":"https://doi.org/10.1142/s2301385023310027","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124059550","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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