Pub Date : 2022-10-28DOI: 10.1109/ICUS55513.2022.9986866
Zijie Dai, Fuqiang Wang, Dong Nan, Yongpeng Weng
This paper studies the leader-follower formation control problem of unknown multiple quadrotors unmanned aerial vehicles (multi-QUAVs) with external disturbances and unmodeled dynamics in three-dimensional space, whereby a data-driven fast terminal sliding mode formation control (DFTSMFC) scheme is studied. Firstly, for multi-QUAV systems with unavailable dynamics, the equivalent full-format dynamic linearization based data models, via nonparametric dynamic linearization technology (DLT), are established. Then, with the aid of fast terminal sliding mode control (FTSMC) strategy and data-driven control (DDC) technique, a DFTSMFC scheme is further designed, which only relies on the input/output (I/O) data of multi-QUAVs, and also improves the robustness of the QUAV systems. At last, the effectiveness of the proposed method is verified by simulation studies.
{"title":"Data-driven Fast Terminal Sliding Mode Formation Control of Unknown Multi-UAVs","authors":"Zijie Dai, Fuqiang Wang, Dong Nan, Yongpeng Weng","doi":"10.1109/ICUS55513.2022.9986866","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986866","url":null,"abstract":"This paper studies the leader-follower formation control problem of unknown multiple quadrotors unmanned aerial vehicles (multi-QUAVs) with external disturbances and unmodeled dynamics in three-dimensional space, whereby a data-driven fast terminal sliding mode formation control (DFTSMFC) scheme is studied. Firstly, for multi-QUAV systems with unavailable dynamics, the equivalent full-format dynamic linearization based data models, via nonparametric dynamic linearization technology (DLT), are established. Then, with the aid of fast terminal sliding mode control (FTSMC) strategy and data-driven control (DDC) technique, a DFTSMFC scheme is further designed, which only relies on the input/output (I/O) data of multi-QUAVs, and also improves the robustness of the QUAV systems. At last, the effectiveness of the proposed method is verified by simulation studies.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124543370","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9986759
Lin Bin, Chen Wei, H. Duan
For Multi-Unmanned Aerial Vehicle (UAV) confrontation, there are many challenging problems such as task assignment and autonomous decision-making. Inspired by the target selection behavior of Swainson's hawk and cooperative hunting of falcons, a grouped attack strategy of Multi-UAV imitating hawk hunting behaviors is proposed to solve the target assignment and teammate assignment problem during the Multi-UAV confrontation when casualties occur on both sides especially. The grouped attack strategy of Multi-UAV consists of a hawk target selection mechanism and a multi-hawk cooperative capture success mechanism. The simulation results demonstrate that the proposed strategy can make allies destroy more enemies.
{"title":"Grouped Attack Strategy of Multi-UAV Imitating Hawk Hunting Behaviors","authors":"Lin Bin, Chen Wei, H. Duan","doi":"10.1109/ICUS55513.2022.9986759","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986759","url":null,"abstract":"For Multi-Unmanned Aerial Vehicle (UAV) confrontation, there are many challenging problems such as task assignment and autonomous decision-making. Inspired by the target selection behavior of Swainson's hawk and cooperative hunting of falcons, a grouped attack strategy of Multi-UAV imitating hawk hunting behaviors is proposed to solve the target assignment and teammate assignment problem during the Multi-UAV confrontation when casualties occur on both sides especially. The grouped attack strategy of Multi-UAV consists of a hawk target selection mechanism and a multi-hawk cooperative capture success mechanism. The simulation results demonstrate that the proposed strategy can make allies destroy more enemies.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124629261","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9987157
Jiaqi Chen, Zhiyong Du, Shiyu Li
The rapidly changing propagation environment during flight is a huge challenge for multi-UAV systems. The wireless channel changing with the propagation environment has an important impact on the information sharing between UAVs, especially in urban scenario. However, the recent research on the statistical properties of Air to Air (A2A) channels cannot be directly used in the control link connectivity analysis due to the complexity. In this paper, a simplified A2A channel model based on Fresnel propagation theory is proposed and applied to investigate the connectivity of communication topologies for control information sharing in multi-UAV systems. Considering UAV flying in 3D space, the impact of 3D blockages is focused on in this paper. Theoretical results show that the average strength of edges decreases with the increase of the density of buildings, which implies that the connectivity between UAVs deteriorate during the UAV flight from the suburbs to the city. What's more, the connectivity of the multi-UAV systems is weakened with the increase of the building density and increased with the increase of the size of multi-UAV systems.
飞行过程中快速变化的传播环境是多无人机系统面临的巨大挑战。无线信道随传播环境的变化对无人机间的信息共享有着重要的影响，尤其是在城市场景下。然而，最近对Air to Air (A2A)信道统计特性的研究由于其复杂性，不能直接用于控制链路连通性分析。本文提出了一种基于菲涅耳传播理论的A2A通道简化模型，并将其应用于多无人机系统控制信息共享中通信拓扑的连通性研究。考虑到无人机在三维空间飞行，本文重点研究了三维障碍物对无人机飞行的影响。理论结果表明，边缘的平均强度随着建筑物密度的增加而降低，这意味着无人机从郊区到城市的飞行过程中，无人机之间的连通性变差。多无人机系统的连通性随着建筑密度的增加而减弱，随着多无人机系统规模的增加而增强。
{"title":"Connectivity Analysis of Multi-UAV Communication Systems with Simplified Fresnel Propagation Model in Urban Scenarios","authors":"Jiaqi Chen, Zhiyong Du, Shiyu Li","doi":"10.1109/ICUS55513.2022.9987157","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987157","url":null,"abstract":"The rapidly changing propagation environment during flight is a huge challenge for multi-UAV systems. The wireless channel changing with the propagation environment has an important impact on the information sharing between UAVs, especially in urban scenario. However, the recent research on the statistical properties of Air to Air (A2A) channels cannot be directly used in the control link connectivity analysis due to the complexity. In this paper, a simplified A2A channel model based on Fresnel propagation theory is proposed and applied to investigate the connectivity of communication topologies for control information sharing in multi-UAV systems. Considering UAV flying in 3D space, the impact of 3D blockages is focused on in this paper. Theoretical results show that the average strength of edges decreases with the increase of the density of buildings, which implies that the connectivity between UAVs deteriorate during the UAV flight from the suburbs to the city. What's more, the connectivity of the multi-UAV systems is weakened with the increase of the building density and increased with the increase of the size of multi-UAV systems.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130320382","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9986753
Tao Fu, Yanhua Pang, Bo Chen
Recently, Unmanned Aerial Vehicles have been widely used in the fields of water traffic supervision and maritime sovereignty inspection, becoming an important means of data acquisition. It is crucial to apply deep learning-based target detection technology to UAV edge devices. Traditional detectors are often underperformed when deployed directly to UAVs. One reason is that the amount of UAV imagery data is often limited and insufficient to support the training of deep learning algorithms. The second is that deep learning-based detectors often have huge models and huge amounts of parameters with high computational complexity, making it difficult to deploy them to work effectively on edge mobile devices with extremely limited computational resources and memory. To solve these problems, we proposed a new detection model for UAV view ship target based on YOLOv4. To this end, first, we constructed a satellite remote sensing ship image dataset and used transfer learning to reduce the reliance on model training data. Second, we lightened the model by sparsity training, channel and layer pruning, and then used knowledge distillation techniques to rebound the accuracy. In the end, the model size is reduced by 97.19% and the detection time of a single image is reduced by 39.73% while maintaining high detection accuracy, achieving high precision real-time detection suitable for deployment on edge devices such as UAVs.
{"title":"UAVDet: A Lightweight Fast Detection Model for Marine Ships based on Edge Mobile Devices","authors":"Tao Fu, Yanhua Pang, Bo Chen","doi":"10.1109/ICUS55513.2022.9986753","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986753","url":null,"abstract":"Recently, Unmanned Aerial Vehicles have been widely used in the fields of water traffic supervision and maritime sovereignty inspection, becoming an important means of data acquisition. It is crucial to apply deep learning-based target detection technology to UAV edge devices. Traditional detectors are often underperformed when deployed directly to UAVs. One reason is that the amount of UAV imagery data is often limited and insufficient to support the training of deep learning algorithms. The second is that deep learning-based detectors often have huge models and huge amounts of parameters with high computational complexity, making it difficult to deploy them to work effectively on edge mobile devices with extremely limited computational resources and memory. To solve these problems, we proposed a new detection model for UAV view ship target based on YOLOv4. To this end, first, we constructed a satellite remote sensing ship image dataset and used transfer learning to reduce the reliance on model training data. Second, we lightened the model by sparsity training, channel and layer pruning, and then used knowledge distillation techniques to rebound the accuracy. In the end, the model size is reduced by 97.19% and the detection time of a single image is reduced by 39.73% while maintaining high detection accuracy, achieving high precision real-time detection suitable for deployment on edge devices such as UAVs.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126710299","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9986913
Liangchao Guo, Huyang Zhu, Yuwei Liu, Xiaoliang Sun, Xichao Teng
With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles have been widely used to obtain ground remote sensing images. Due to the limited field of view of a single remote sensing image, it is necessary to stitch the images to obtain a large-scale scene image to apply to various practical applications. This paper introduces a two-stage stitching algorithm combining geographic coordinate information and image features. The stitching of sequence images is divided into intra-line stitching and inter-line stitching. Firstly, we use geographic coordinates and image features to calculate transformation parameters between a row of images, respectively, and select an appropriate transformation matrix to stitch a single row of images based on the rotation parameters in the homography matrix. Then, we also calculate the transformation matrices between different rows of images based on the geographic coordinate information and image features, and select the appropriate transformation matrix based on the comparison of the rotation parameters in different transformation matrices. Stitching experiments are carried out for various scenarios. Compared with traditional stitching methods, the proposed method integrates information from different sources, has higher reliability, and can adapt to stitching of various types of scenarios.
{"title":"Aerial Image Stitching Based on Fusion of Geographic Coordinates and Image Features","authors":"Liangchao Guo, Huyang Zhu, Yuwei Liu, Xiaoliang Sun, Xichao Teng","doi":"10.1109/ICUS55513.2022.9986913","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986913","url":null,"abstract":"With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles have been widely used to obtain ground remote sensing images. Due to the limited field of view of a single remote sensing image, it is necessary to stitch the images to obtain a large-scale scene image to apply to various practical applications. This paper introduces a two-stage stitching algorithm combining geographic coordinate information and image features. The stitching of sequence images is divided into intra-line stitching and inter-line stitching. Firstly, we use geographic coordinates and image features to calculate transformation parameters between a row of images, respectively, and select an appropriate transformation matrix to stitch a single row of images based on the rotation parameters in the homography matrix. Then, we also calculate the transformation matrices between different rows of images based on the geographic coordinate information and image features, and select the appropriate transformation matrix based on the comparison of the rotation parameters in different transformation matrices. Stitching experiments are carried out for various scenarios. Compared with traditional stitching methods, the proposed method integrates information from different sources, has higher reliability, and can adapt to stitching of various types of scenarios.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129169880","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9986806
Xiaohan Liu, Yezhou Yang, Oujie Li, S. Zhang, W. Qian, Guodong Sun, Fu Chen, Junce Ma, Ping Liang, Shaowen Cheng, Kuangyang Shu
Natural disaster observation technology has significant progress with the development of advanced satellite remote sensing (RS), Unmanned Aerial Vehicles (UAVs), and Internet of Things (IOT) technologies. In this paper, we discuss the architecture of an integrated natural disaster observation system using RS, UAVs, and IOT technologies. We concern three aspects: heterogeneous communication, air-ground cooperation mechanism, and data visualization using Geographic Information System (GIS). A Real-world system has been developed, and a series of simulation and experiments have been performed, and the proposed architecture provides better system scalability in complex environment.
{"title":"An Integrated Natural Disaster Observation System","authors":"Xiaohan Liu, Yezhou Yang, Oujie Li, S. Zhang, W. Qian, Guodong Sun, Fu Chen, Junce Ma, Ping Liang, Shaowen Cheng, Kuangyang Shu","doi":"10.1109/ICUS55513.2022.9986806","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986806","url":null,"abstract":"Natural disaster observation technology has significant progress with the development of advanced satellite remote sensing (RS), Unmanned Aerial Vehicles (UAVs), and Internet of Things (IOT) technologies. In this paper, we discuss the architecture of an integrated natural disaster observation system using RS, UAVs, and IOT technologies. We concern three aspects: heterogeneous communication, air-ground cooperation mechanism, and data visualization using Geographic Information System (GIS). A Real-world system has been developed, and a series of simulation and experiments have been performed, and the proposed architecture provides better system scalability in complex environment.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121280871","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9987019
Yicong Tong, Jianming Miao, Yanyun Wang
A dynamic event-triggered leader-following consensus algorithm is proposed for a multi autonomous underwater vehicle (AUV) system. The nonlinear AUV models are simplified through standard feedback linearization process. For both communication layer and controller layer, the dynamic state-dependent event-triggered technique is used to save AUV's limited communication resources, respectively. Stability analysis is accomplished through Lyapunov method and Zeno behavior is excluded. Numerical simulation illustrates the effectiveness of the proposed algorithm.
{"title":"A dynamic event-triggered leader-following consensus algorithm for multi-AUVs system","authors":"Yicong Tong, Jianming Miao, Yanyun Wang","doi":"10.1109/ICUS55513.2022.9987019","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987019","url":null,"abstract":"A dynamic event-triggered leader-following consensus algorithm is proposed for a multi autonomous underwater vehicle (AUV) system. The nonlinear AUV models are simplified through standard feedback linearization process. For both communication layer and controller layer, the dynamic state-dependent event-triggered technique is used to save AUV's limited communication resources, respectively. Stability analysis is accomplished through Lyapunov method and Zeno behavior is excluded. Numerical simulation illustrates the effectiveness of the proposed algorithm.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114161750","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9986528
Q. Shi, Hua Wang, Hao Cheng, Tao Han, Jiachao Guo
Owing to the advantages of target tracking algorithm based on correlation filtering on the efficiency in speed and the tracking accuracy, it has been widely applied in the image real-time tracking, especially in the field of the video surveillance, the human-computer interaction, and the intelligent transportation. However, considering the tracking timeliness and the ability to deal with deformation, position changes and occlusion in complex background, especially in the application for the unmanned aerial vehicle (UAV) reconnaissance, an adaptive multi-feature fusion improved tracking algorithm with confidence judgement strategy is proposed in this paper. On the basis of the efficient convolution operators handle-crafted (ECO-HC) method, which is the best algorithm with excellent performance based on correlation filtering, an adaptive multi-feature strategy and a strategy of confidence state recognition with confidence judgement and relocation are described in detail. After quantitative and qualitative comparison tests with other advanced algorithms, the results fully verify the superiority in tracking accuracy and robustness against the interference of the complex background.
{"title":"Adaptive Multi-feature Fusion Improved ECO-HC Image Tracking Algorithm Based on Confidence Judgement for UAV Reconnaissance","authors":"Q. Shi, Hua Wang, Hao Cheng, Tao Han, Jiachao Guo","doi":"10.1109/ICUS55513.2022.9986528","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986528","url":null,"abstract":"Owing to the advantages of target tracking algorithm based on correlation filtering on the efficiency in speed and the tracking accuracy, it has been widely applied in the image real-time tracking, especially in the field of the video surveillance, the human-computer interaction, and the intelligent transportation. However, considering the tracking timeliness and the ability to deal with deformation, position changes and occlusion in complex background, especially in the application for the unmanned aerial vehicle (UAV) reconnaissance, an adaptive multi-feature fusion improved tracking algorithm with confidence judgement strategy is proposed in this paper. On the basis of the efficient convolution operators handle-crafted (ECO-HC) method, which is the best algorithm with excellent performance based on correlation filtering, an adaptive multi-feature strategy and a strategy of confidence state recognition with confidence judgement and relocation are described in detail. After quantitative and qualitative comparison tests with other advanced algorithms, the results fully verify the superiority in tracking accuracy and robustness against the interference of the complex background.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114170506","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9986736
Dengfeng Yang, Xiaodong Yan
This paper establishes the cooperative active defense mathematical model and the distributed guidance strategy of TADs system in the two-dimensional scenario. The TADs system has a target, an attacker and multi defenders, which is different from the traditional three-body TAD system. The distributed guidance strategy makes the target and the defenders to cooperatively intercept the attacker and cover its escape region. In addition, nonlinear model predictive control (NMPC) is used to solve the problem. The guidance strategy considers the realistic model of TADs system, the defenders and target input constraints, and the target safety domain constraints. The NMPC method is used to optimize and calculate the cooperative input quantity of the target and the cooperative proportional coefficients for the defenders. The simulation results show that the distributed guidance strategy can realize the cooperative active defense guidance of the target and the defenders in the TADs system and realize the interception and escape region coverage of the attacker.
{"title":"Distributed Collaborative Active Defense Guidance for TADs System via NMPC","authors":"Dengfeng Yang, Xiaodong Yan","doi":"10.1109/ICUS55513.2022.9986736","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9986736","url":null,"abstract":"This paper establishes the cooperative active defense mathematical model and the distributed guidance strategy of TADs system in the two-dimensional scenario. The TADs system has a target, an attacker and multi defenders, which is different from the traditional three-body TAD system. The distributed guidance strategy makes the target and the defenders to cooperatively intercept the attacker and cover its escape region. In addition, nonlinear model predictive control (NMPC) is used to solve the problem. The guidance strategy considers the realistic model of TADs system, the defenders and target input constraints, and the target safety domain constraints. The NMPC method is used to optimize and calculate the cooperative input quantity of the target and the cooperative proportional coefficients for the defenders. The simulation results show that the distributed guidance strategy can realize the cooperative active defense guidance of the target and the defenders in the TADs system and realize the interception and escape region coverage of the attacker.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"576 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114310810","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 : 2022-10-28DOI: 10.1109/ICUS55513.2022.9987142
Yanrong Lu, Jia Zhu, Zhiwen Wang, Nan Du, Jindou Zhang
For the autonomous vehicles path tracking problem, a preview tracking strategy is proposed based on model predictive control (MPC) in this paper. First, under the scenario that the road ahead information is available, an augmented error system is constructed based on the prediction tracking error, state difference and preview information. By introducing an appropriate objective function, the original path preview tracking problem is transformed into an optimization problem of the unconstrained predictive control. Second, the optimal prediction control sequence is obtained according to the minimum value principle, and its first component, which contains the non-causal tracking error, state feedback and preview feedforward, is utilized to perform the receding-horizon control. Furthermore, the optimization problem of the constrained predictive control is also considered. Finally, the platform of CarSim and Simulink is adopted to verify the effectiveness of the path preview tracking.
{"title":"Path Preview Tracking for Autonomous Vehicles Based on Model Predictive Control","authors":"Yanrong Lu, Jia Zhu, Zhiwen Wang, Nan Du, Jindou Zhang","doi":"10.1109/ICUS55513.2022.9987142","DOIUrl":"https://doi.org/10.1109/ICUS55513.2022.9987142","url":null,"abstract":"For the autonomous vehicles path tracking problem, a preview tracking strategy is proposed based on model predictive control (MPC) in this paper. First, under the scenario that the road ahead information is available, an augmented error system is constructed based on the prediction tracking error, state difference and preview information. By introducing an appropriate objective function, the original path preview tracking problem is transformed into an optimization problem of the unconstrained predictive control. Second, the optimal prediction control sequence is obtained according to the minimum value principle, and its first component, which contains the non-causal tracking error, state feedback and preview feedforward, is utilized to perform the receding-horizon control. Furthermore, the optimization problem of the constrained predictive control is also considered. Finally, the platform of CarSim and Simulink is adopted to verify the effectiveness of the path preview tracking.","PeriodicalId":345773,"journal":{"name":"2022 IEEE International Conference on Unmanned Systems (ICUS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126502014","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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