A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic Model
This paper mainly studies the obstacle avoidance and rapid reconstruction of UAV formations. A hybrid trajectory planning algorithm based on potential field fluid dynamic model and bidirectional fast search random tree is proposed to improve the ability of UAV formation to adapt to complex dynamic e...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8939374/ |
| _version_ | 1818622033076944896 |
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| author | Jie Huang Wei Sun Yu Gao |
| author_facet | Jie Huang Wei Sun Yu Gao |
| author_sort | Jie Huang |
| collection | DOAJ |
| description | This paper mainly studies the obstacle avoidance and rapid reconstruction of UAV formations. A hybrid trajectory planning algorithm based on potential field fluid dynamic model and bidirectional fast search random tree is proposed to improve the ability of UAV formation to adapt to complex dynamic environment. Firstly, a dynamic system mathematical model based on fluid potential energy field is proposed; and the obstacle potential energy function and potential energy function between the formations modify the disturbance flow field. Secondly, IBi-directional Rapidly Exploring Random Tree (IBi-RRT) algorithm with adaptive step size is scheduled to solve the dispersive and convergent streamlines of disturbed flow field and to plan the trajectory. This method can clarify the flow field streamlines by adaptive step size combined with rolling detection method, which greatly improves the formation's ability to avoid dynamic threats. The experimental results show that the proposed improved fluid potential energy field dynamic system and IBi-RRT hybrid trajectory planning algorithm with adaptive step size can effectively improve the adaptive ability of UAV formation to the dynamic environment, and can plan the ideal trajectory in response to unexpected situations. |
| first_indexed | 2024-12-16T18:18:44Z |
| format | Article |
| id | doaj.art-747cd9fa8c5741ec8b95527127736bfa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:18:44Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-747cd9fa8c5741ec8b95527127736bfa2022-12-21T22:21:37ZengIEEEIEEE Access2169-35362020-01-0182824283410.1109/ACCESS.2019.29616328939374A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic ModelJie Huang0https://orcid.org/0000-0003-2158-8168Wei Sun1https://orcid.org/0000-0001-7469-8999Yu Gao2https://orcid.org/0000-0001-7407-254XSchool of Aerospace Science and Technology, Xidian University, Xi’an, ChinaSchool of Aerospace Science and Technology, Xidian University, Xi’an, ChinaSchool of Aerospace Science and Technology, Xidian University, Xi’an, ChinaThis paper mainly studies the obstacle avoidance and rapid reconstruction of UAV formations. A hybrid trajectory planning algorithm based on potential field fluid dynamic model and bidirectional fast search random tree is proposed to improve the ability of UAV formation to adapt to complex dynamic environment. Firstly, a dynamic system mathematical model based on fluid potential energy field is proposed; and the obstacle potential energy function and potential energy function between the formations modify the disturbance flow field. Secondly, IBi-directional Rapidly Exploring Random Tree (IBi-RRT) algorithm with adaptive step size is scheduled to solve the dispersive and convergent streamlines of disturbed flow field and to plan the trajectory. This method can clarify the flow field streamlines by adaptive step size combined with rolling detection method, which greatly improves the formation's ability to avoid dynamic threats. The experimental results show that the proposed improved fluid potential energy field dynamic system and IBi-RRT hybrid trajectory planning algorithm with adaptive step size can effectively improve the adaptive ability of UAV formation to the dynamic environment, and can plan the ideal trajectory in response to unexpected situations.https://ieeexplore.ieee.org/document/8939374/Complex environmentfluid potential energy fieldobstacles avoidancesudden threats |
| spellingShingle | Jie Huang Wei Sun Yu Gao A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic Model IEEE Access Complex environment fluid potential energy field obstacles avoidance sudden threats |
| title | A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic Model |
| title_full | A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic Model |
| title_fullStr | A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic Model |
| title_full_unstemmed | A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic Model |
| title_short | A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic Model |
| title_sort | method of trajectory planning for unmanned aerial vehicle formation based on fluid dynamic model |
| topic | Complex environment fluid potential energy field obstacles avoidance sudden threats |
| url | https://ieeexplore.ieee.org/document/8939374/ |
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