Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm
Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. Aiming to reduce the influence of non-complete ground threat information on UAH path planning, a ground threat prediction-based path planning method is proposed based on artificia...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-02-01
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| Series: | Defence Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914723001071 |
| _version_ | 1797272505764806656 |
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| author | Zengliang Han Mou Chen Haojie Zhu Qingxian Wu |
| author_facet | Zengliang Han Mou Chen Haojie Zhu Qingxian Wu |
| author_sort | Zengliang Han |
| collection | DOAJ |
| description | Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. Aiming to reduce the influence of non-complete ground threat information on UAH path planning, a ground threat prediction-based path planning method is proposed based on artificial bee colony (ABC) algorithm by collaborative thinking strategy. Firstly, a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats. The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time. Then, a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats. By adding the collision warning mechanism to the path planning model, the flight path could be dynamically adjusted according to changing no-fly zones. Furthermore, a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy. The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution, and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy, which makes the optimization performance of ABC algorithm more controllable and efficient. Finally, simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method. |
| first_indexed | 2024-03-07T14:29:21Z |
| format | Article |
| id | doaj.art-b2c958ee4d7e40969f6d3eb18a8c2b19 |
| institution | Directory Open Access Journal |
| issn | 2214-9147 |
| language | English |
| last_indexed | 2024-03-07T14:29:21Z |
| publishDate | 2024-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Defence Technology |
| spelling | doaj.art-b2c958ee4d7e40969f6d3eb18a8c2b192024-03-06T05:26:57ZengKeAi Communications Co., Ltd.Defence Technology2214-91472024-02-0132122Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithmZengliang Han0Mou Chen1Haojie Zhu2Qingxian Wu3School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCorresponding author.; School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaUnmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. Aiming to reduce the influence of non-complete ground threat information on UAH path planning, a ground threat prediction-based path planning method is proposed based on artificial bee colony (ABC) algorithm by collaborative thinking strategy. Firstly, a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats. The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time. Then, a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats. By adding the collision warning mechanism to the path planning model, the flight path could be dynamically adjusted according to changing no-fly zones. Furthermore, a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy. The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution, and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy, which makes the optimization performance of ABC algorithm more controllable and efficient. Finally, simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.http://www.sciencedirect.com/science/article/pii/S2214914723001071UAHPath planningGround threat predictionHybrid enhancedCollaborative thinking |
| spellingShingle | Zengliang Han Mou Chen Haojie Zhu Qingxian Wu Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm Defence Technology UAH Path planning Ground threat prediction Hybrid enhanced Collaborative thinking |
| title | Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm |
| title_full | Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm |
| title_fullStr | Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm |
| title_full_unstemmed | Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm |
| title_short | Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm |
| title_sort | ground threat prediction based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm |
| topic | UAH Path planning Ground threat prediction Hybrid enhanced Collaborative thinking |
| url | http://www.sciencedirect.com/science/article/pii/S2214914723001071 |
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