Decentralized and autonomous behavior decision-making for UAV cluster
It is difficult for conventional methods like the diagram theory in a complex environment to carry out modeling and calculation so as to make large-scale cluster behavior decisions. Hence, this paper studies small fixed wings and establishes the decentralized behavior decision-making model for a UAV...
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| Format: | Article |
| Language: | zho |
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EDP Sciences
2024-12-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1030/jnwpu2024426p1030.html |
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| _version_ | 1825206540025462784 |
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| author | HU Weijun ZHANG Weijie YIN Wei XIONG Jingyi |
| author_facet | HU Weijun ZHANG Weijie YIN Wei XIONG Jingyi |
| author_sort | HU Weijun |
| collection | DOAJ |
| description | It is difficult for conventional methods like the diagram theory in a complex environment to carry out modeling and calculation so as to make large-scale cluster behavior decisions. Hence, this paper studies small fixed wings and establishes the decentralized behavior decision-making model for a UAV cluster that has communication limitations and scale ceiling effects. The idea of swarm intelligence is combined with the decoupling multi-agent deep deterministic strategy gradient (DE-MADDPG) for the constructed model to do adaptive learning. Finally, the optimal behavior decision of the UAV cluster is made. Simulations are carried out to verify the model. The consistent movement of the UAV cluster and the maneuvering obstacle avoidance behavior in complex environments are realized. Compared with the MADDPG, the DE-MADDPG exhibits superior precision and real-time capability. |
| format | Article |
| id | doaj-art-069f50e1b9e342248e5fa11679e3a2f5 |
| institution | Kabale University |
| issn | 1000-2758 2609-7125 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj-art-069f50e1b9e342248e5fa11679e3a2f52025-02-07T08:23:13ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252024-12-014261030103810.1051/jnwpu/20244261030jnwpu2024426p1030Decentralized and autonomous behavior decision-making for UAV clusterHU Weijun0ZHANG Weijie1YIN Wei2XIONG Jingyi3School of Astronautics, Northwestern Polytechnical UniversitySchool of Astronautics, Northwestern Polytechnical UniversityShanghai Institute of Mechanical and Electrical EngineeringSchool of Astronautics, Northwestern Polytechnical UniversityIt is difficult for conventional methods like the diagram theory in a complex environment to carry out modeling and calculation so as to make large-scale cluster behavior decisions. Hence, this paper studies small fixed wings and establishes the decentralized behavior decision-making model for a UAV cluster that has communication limitations and scale ceiling effects. The idea of swarm intelligence is combined with the decoupling multi-agent deep deterministic strategy gradient (DE-MADDPG) for the constructed model to do adaptive learning. Finally, the optimal behavior decision of the UAV cluster is made. Simulations are carried out to verify the model. The consistent movement of the UAV cluster and the maneuvering obstacle avoidance behavior in complex environments are realized. Compared with the MADDPG, the DE-MADDPG exhibits superior precision and real-time capability.https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1030/jnwpu2024426p1030.htmluav clusterautonomous behavior decision-makingmulti-agent deep reinforcement learningdecentralizationconsistent movementobstacle avoidance |
| spellingShingle | HU Weijun ZHANG Weijie YIN Wei XIONG Jingyi Decentralized and autonomous behavior decision-making for UAV cluster Xibei Gongye Daxue Xuebao uav cluster autonomous behavior decision-making multi-agent deep reinforcement learning decentralization consistent movement obstacle avoidance |
| title | Decentralized and autonomous behavior decision-making for UAV cluster |
| title_full | Decentralized and autonomous behavior decision-making for UAV cluster |
| title_fullStr | Decentralized and autonomous behavior decision-making for UAV cluster |
| title_full_unstemmed | Decentralized and autonomous behavior decision-making for UAV cluster |
| title_short | Decentralized and autonomous behavior decision-making for UAV cluster |
| title_sort | decentralized and autonomous behavior decision making for uav cluster |
| topic | uav cluster autonomous behavior decision-making multi-agent deep reinforcement learning decentralization consistent movement obstacle avoidance |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2024/06/jnwpu2024426p1030/jnwpu2024426p1030.html |
| work_keys_str_mv | AT huweijun decentralizedandautonomousbehaviordecisionmakingforuavcluster AT zhangweijie decentralizedandautonomousbehaviordecisionmakingforuavcluster AT yinwei decentralizedandautonomousbehaviordecisionmakingforuavcluster AT xiongjingyi decentralizedandautonomousbehaviordecisionmakingforuavcluster |