A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning
Large Unmanned Aerial Vehicle (UAV) clusters, containing hundreds of UAVs, have widely been used in the modern world. Therein, mission planning is the core of large UAV cluster collaborative systems. In this paper, we propose a mission planning method by introducing the Simple Attention Model (SAM)...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/6/1304 |
| _version_ | 1827626043076247552 |
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| author | Lei Zuo Shan Gao Yachao Li Lianghai Li Ming Li Xiaofei Lu |
| author_facet | Lei Zuo Shan Gao Yachao Li Lianghai Li Ming Li Xiaofei Lu |
| author_sort | Lei Zuo |
| collection | DOAJ |
| description | Large Unmanned Aerial Vehicle (UAV) clusters, containing hundreds of UAVs, have widely been used in the modern world. Therein, mission planning is the core of large UAV cluster collaborative systems. In this paper, we propose a mission planning method by introducing the Simple Attention Model (SAM) into Dynamic Information Reinforcement Learning (DIRL), named DIRL-SAM. To reduce the computational complexity of the original attention model, we derive the SAM with a lightweight interactive model to rapidly extract high-dimensional features of the cluster information. In DIRL, dynamic training conditions are considered to simulate different mission environments. Meanwhile, the data expansion in DIRL guarantees the convergence of the model in these dynamic environments, which improves the robustness of the algorithm. Finally, the simulation experiment results show that the proposed method can adaptively provide feasible mission planning schemes with second-level solution speed and that it exhibits excellent generalization performance in large-scale cluster planning problems. |
| first_indexed | 2024-03-09T12:47:14Z |
| format | Article |
| id | doaj.art-c1cfe7e8656442b982a7f0f0e4301cd3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:47:14Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c1cfe7e8656442b982a7f0f0e4301cd32023-11-30T22:10:48ZengMDPI AGRemote Sensing2072-42922022-03-01146130410.3390/rs14061304A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission PlanningLei Zuo0Shan Gao1Yachao Li2Lianghai Li3Ming Li4Xiaofei Lu5National Lab of Radar Signal Processing, Xidian University, Xi’an 710000, ChinaNational Lab of Radar Signal Processing, Xidian University, Xi’an 710000, ChinaNational Lab of Radar Signal Processing, Xidian University, Xi’an 710000, ChinaBeijing Research Institute of Telemetry, Beijing 100076, ChinaNational Lab of Radar Signal Processing, Xidian University, Xi’an 710000, ChinaJiuquan Satelite Launch Centre, Jiuquan 735400, ChinaLarge Unmanned Aerial Vehicle (UAV) clusters, containing hundreds of UAVs, have widely been used in the modern world. Therein, mission planning is the core of large UAV cluster collaborative systems. In this paper, we propose a mission planning method by introducing the Simple Attention Model (SAM) into Dynamic Information Reinforcement Learning (DIRL), named DIRL-SAM. To reduce the computational complexity of the original attention model, we derive the SAM with a lightweight interactive model to rapidly extract high-dimensional features of the cluster information. In DIRL, dynamic training conditions are considered to simulate different mission environments. Meanwhile, the data expansion in DIRL guarantees the convergence of the model in these dynamic environments, which improves the robustness of the algorithm. Finally, the simulation experiment results show that the proposed method can adaptively provide feasible mission planning schemes with second-level solution speed and that it exhibits excellent generalization performance in large-scale cluster planning problems.https://www.mdpi.com/2072-4292/14/6/1304mission planningUAV clusterreinforcement learningattention modelcombinational optimization |
| spellingShingle | Lei Zuo Shan Gao Yachao Li Lianghai Li Ming Li Xiaofei Lu A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning Remote Sensing mission planning UAV cluster reinforcement learning attention model combinational optimization |
| title | A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning |
| title_full | A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning |
| title_fullStr | A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning |
| title_full_unstemmed | A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning |
| title_short | A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning |
| title_sort | fast and robust algorithm with reinforcement learning for large uav cluster mission planning |
| topic | mission planning UAV cluster reinforcement learning attention model combinational optimization |
| url | https://www.mdpi.com/2072-4292/14/6/1304 |
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