Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution Algorithm
With the widespread application of unmanned aerial vehicles (UAVs) in civilian and military fields, how to effectively detect and resolve conflicts of large-volume and high-density UAV flights in local airspace has become an important issue. This paper proposes a method for UAV conflict detection an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/12/1008 |
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| author | Zhichong Zhou Guhao Zhao Yiru Jiang Yarong Wu Jiale Yang Lingzhong Meng |
| author_facet | Zhichong Zhou Guhao Zhao Yiru Jiang Yarong Wu Jiale Yang Lingzhong Meng |
| author_sort | Zhichong Zhou |
| collection | DOAJ |
| description | With the widespread application of unmanned aerial vehicles (UAVs) in civilian and military fields, how to effectively detect and resolve conflicts of large-volume and high-density UAV flights in local airspace has become an important issue. This paper proposes a method for UAV conflict detection and resolution based on tensor operation and an improved differential algorithm. Firstly, the UAV protection zone model and airspace rasterization model are constructed, and the rapid detection of flight conflicts is achieved by using the properties of tensor Hadamard product operations and prime factorization. Then, for the detected conflicts, a hybrid improved differential evolution algorithm is used for resolution. This algorithm improves the solution speed and quality by using an adaptive mutation operator and introducing a redundant evaluation mechanism and a confidence-based selection strategy. Simulation results show that this method can quickly and accurately detect and resolve flight conflicts in high-density UAV scenarios, with high timeliness and conflict resolution capability. |
| format | Article |
| id | doaj-art-12b428d9d4384e44b4002e21bc0b738c |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-12b428d9d4384e44b4002e21bc0b738c2024-12-27T14:02:31ZengMDPI AGAerospace2226-43102024-12-011112100810.3390/aerospace11121008Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution AlgorithmZhichong Zhou0Guhao Zhao1Yiru Jiang2Yarong Wu3Jiale Yang4Lingzhong Meng5Air Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710051, ChinaAir Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710051, ChinaAir Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710051, ChinaAir Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710051, ChinaAir Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710051, ChinaAir Traffic Control and Navigation School, Air Force Engineering University, Xi’an 710051, ChinaWith the widespread application of unmanned aerial vehicles (UAVs) in civilian and military fields, how to effectively detect and resolve conflicts of large-volume and high-density UAV flights in local airspace has become an important issue. This paper proposes a method for UAV conflict detection and resolution based on tensor operation and an improved differential algorithm. Firstly, the UAV protection zone model and airspace rasterization model are constructed, and the rapid detection of flight conflicts is achieved by using the properties of tensor Hadamard product operations and prime factorization. Then, for the detected conflicts, a hybrid improved differential evolution algorithm is used for resolution. This algorithm improves the solution speed and quality by using an adaptive mutation operator and introducing a redundant evaluation mechanism and a confidence-based selection strategy. Simulation results show that this method can quickly and accurately detect and resolve flight conflicts in high-density UAV scenarios, with high timeliness and conflict resolution capability.https://www.mdpi.com/2226-4310/11/12/1008unmanned aerial vehiclesflight conflict detectiontensor Hadamard productprime factorizationdifferential evolution algorithmconflict resolution |
| spellingShingle | Zhichong Zhou Guhao Zhao Yiru Jiang Yarong Wu Jiale Yang Lingzhong Meng Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution Algorithm Aerospace unmanned aerial vehicles flight conflict detection tensor Hadamard product prime factorization differential evolution algorithm conflict resolution |
| title | Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution Algorithm |
| title_full | Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution Algorithm |
| title_fullStr | Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution Algorithm |
| title_full_unstemmed | Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution Algorithm |
| title_short | Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution Algorithm |
| title_sort | research on uav conflict detection and resolution based on tensor operation and improved differential evolution algorithm |
| topic | unmanned aerial vehicles flight conflict detection tensor Hadamard product prime factorization differential evolution algorithm conflict resolution |
| url | https://www.mdpi.com/2226-4310/11/12/1008 |
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