Deep Reinforcement Learning for UAV Intelligent Mission Planning
Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the tradition...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/3551508 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849305057839808512 |
|---|---|
| author | Longfei Yue Rennong Yang Ying Zhang Lixin Yu Zhuangzhuang Wang |
| author_facet | Longfei Yue Rennong Yang Ying Zhang Lixin Yu Zhuangzhuang Wang |
| author_sort | Longfei Yue |
| collection | DOAJ |
| description | Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense (SEAD) mission planning is described as a sequential decision-making problem and formalized as a Markov decision process (MDP). Then, the SEAD intelligent planning model based on the proximal policy optimization (PPO) algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy. |
| format | Article |
| id | doaj-art-08a5afbc8def420f8ea9bc1fd5da4623 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-08a5afbc8def420f8ea9bc1fd5da46232025-08-20T03:55:33ZengWileyComplexity1099-05262022-01-01202210.1155/2022/3551508Deep Reinforcement Learning for UAV Intelligent Mission PlanningLongfei Yue0Rennong Yang1Ying Zhang2Lixin Yu3Zhuangzhuang Wang4Air Traffic Control and Navigation CollegeAir Traffic Control and Navigation CollegeAir Traffic Control and Navigation CollegeAir Traffic Control and Navigation CollegeAviation Maintenance NCO SchoolRapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense (SEAD) mission planning is described as a sequential decision-making problem and formalized as a Markov decision process (MDP). Then, the SEAD intelligent planning model based on the proximal policy optimization (PPO) algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy.http://dx.doi.org/10.1155/2022/3551508 |
| spellingShingle | Longfei Yue Rennong Yang Ying Zhang Lixin Yu Zhuangzhuang Wang Deep Reinforcement Learning for UAV Intelligent Mission Planning Complexity |
| title | Deep Reinforcement Learning for UAV Intelligent Mission Planning |
| title_full | Deep Reinforcement Learning for UAV Intelligent Mission Planning |
| title_fullStr | Deep Reinforcement Learning for UAV Intelligent Mission Planning |
| title_full_unstemmed | Deep Reinforcement Learning for UAV Intelligent Mission Planning |
| title_short | Deep Reinforcement Learning for UAV Intelligent Mission Planning |
| title_sort | deep reinforcement learning for uav intelligent mission planning |
| url | http://dx.doi.org/10.1155/2022/3551508 |
| work_keys_str_mv | AT longfeiyue deepreinforcementlearningforuavintelligentmissionplanning AT rennongyang deepreinforcementlearningforuavintelligentmissionplanning AT yingzhang deepreinforcementlearningforuavintelligentmissionplanning AT lixinyu deepreinforcementlearningforuavintelligentmissionplanning AT zhuangzhuangwang deepreinforcementlearningforuavintelligentmissionplanning |