Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement Learning
Unmanned aerial vehicle(UAV)with the advantages of high effectiveness and flexible autonomy has gradually replaced manned aircraft to combat,and multi-UAV cooperative combat mission planning becomes the hot research issue.An end-to-end cooperative attack intelligent planning method for multi-UAV bas...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Advances in Aeronautical Science and Engineering
2022-12-01
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| Series: | Hangkong gongcheng jinzhan |
| Subjects: | |
| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2022017?st=article_issue |
| _version_ | 1828035778153807872 |
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| author | LI Junsheng YUE Longfei ZUO Jialiang YU Lixin ZHAO Jiale |
| author_facet | LI Junsheng YUE Longfei ZUO Jialiang YU Lixin ZHAO Jiale |
| author_sort | LI Junsheng |
| collection | DOAJ |
| description | Unmanned aerial vehicle(UAV)with the advantages of high effectiveness and flexible autonomy has gradually replaced manned aircraft to combat,and multi-UAV cooperative combat mission planning becomes the hot research issue.An end-to-end cooperative attack intelligent planning method for multi-UAV based on deep reinforcement learning(DRL)is presented to overcome the shortcomings of traditional mission planning algorithms,such as static dependence,low-dimensional simple scenarios and slow on-board computing speed.The suppression of enemy air defense(SEAD)mission planning is modeled as the Markov decision process.The SEAD intelligent planning model based on proximal policy optimization(PPO)algorithm is established,and two groups of experiments are used to verify the effectiveness and robustness of the intelligent planning model.The results show that the DRL-based intelligent planning method can realize fast and fine planning,adapt to unknown,continuous and high-dimensional environment situation.The SEAD intelligent planning model has the capacity of tactics cooperative planning. |
| first_indexed | 2024-04-10T15:51:27Z |
| format | Article |
| id | doaj.art-29999897dae141a18b53245a57d017e5 |
| institution | Directory Open Access Journal |
| issn | 1674-8190 |
| language | zho |
| last_indexed | 2024-04-10T15:51:27Z |
| publishDate | 2022-12-01 |
| publisher | Editorial Department of Advances in Aeronautical Science and Engineering |
| record_format | Article |
| series | Hangkong gongcheng jinzhan |
| spelling | doaj.art-29999897dae141a18b53245a57d017e52023-02-11T05:28:14ZzhoEditorial Department of Advances in Aeronautical Science and EngineeringHangkong gongcheng jinzhan1674-81902022-12-011364049,9610.16615/j.cnki.1674-8190.2022.06.0420220604Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement LearningLI Junsheng0YUE Longfei1ZUO Jialiang2YU Lixin3ZHAO Jiale4College of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaUnmanned aerial vehicle(UAV)with the advantages of high effectiveness and flexible autonomy has gradually replaced manned aircraft to combat,and multi-UAV cooperative combat mission planning becomes the hot research issue.An end-to-end cooperative attack intelligent planning method for multi-UAV based on deep reinforcement learning(DRL)is presented to overcome the shortcomings of traditional mission planning algorithms,such as static dependence,low-dimensional simple scenarios and slow on-board computing speed.The suppression of enemy air defense(SEAD)mission planning is modeled as the Markov decision process.The SEAD intelligent planning model based on proximal policy optimization(PPO)algorithm is established,and two groups of experiments are used to verify the effectiveness and robustness of the intelligent planning model.The results show that the DRL-based intelligent planning method can realize fast and fine planning,adapt to unknown,continuous and high-dimensional environment situation.The SEAD intelligent planning model has the capacity of tactics cooperative planning.http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2022017?st=article_issuemulti-uavdeep learningdeep reinforcement learningppo algorithmgeneralizationcooperative combat |
| spellingShingle | LI Junsheng YUE Longfei ZUO Jialiang YU Lixin ZHAO Jiale Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement Learning Hangkong gongcheng jinzhan multi-uav deep learning deep reinforcement learning ppo algorithm generalization cooperative combat |
| title | Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement Learning |
| title_full | Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement Learning |
| title_fullStr | Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement Learning |
| title_full_unstemmed | Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement Learning |
| title_short | Multi-UAV Cooperative Offensive Combat Intelligent Planning Based on Deep Reinforcement Learning |
| title_sort | multi uav cooperative offensive combat intelligent planning based on deep reinforcement learning |
| topic | multi-uav deep learning deep reinforcement learning ppo algorithm generalization cooperative combat |
| url | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2022017?st=article_issue |
| work_keys_str_mv | AT lijunsheng multiuavcooperativeoffensivecombatintelligentplanningbasedondeepreinforcementlearning AT yuelongfei multiuavcooperativeoffensivecombatintelligentplanningbasedondeepreinforcementlearning AT zuojialiang multiuavcooperativeoffensivecombatintelligentplanningbasedondeepreinforcementlearning AT yulixin multiuavcooperativeoffensivecombatintelligentplanningbasedondeepreinforcementlearning AT zhaojiale multiuavcooperativeoffensivecombatintelligentplanningbasedondeepreinforcementlearning |