UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment
The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9615093/ |
| _version_ | 1818988318022434816 |
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| author | Jianxin Feng Jingze Zhang Geng Zhang Shuang Xie Yuanming Ding Zhiguo Liu |
| author_facet | Jianxin Feng Jingze Zhang Geng Zhang Shuang Xie Yuanming Ding Zhiguo Liu |
| author_sort | Jianxin Feng |
| collection | DOAJ |
| description | The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can finally fly a safer path with high accuracy in an unknown environment with static or dynamic obstacles, and avoid local minima or solve local oscillation at the same time. |
| first_indexed | 2024-12-20T19:20:40Z |
| format | Article |
| id | doaj.art-a4fbdba7944c4993aa1be9f60ffc22d0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T19:20:40Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a4fbdba7944c4993aa1be9f60ffc22d02022-12-21T19:29:00ZengIEEEIEEE Access2169-35362021-01-01915467915469110.1109/ACCESS.2021.31282959615093UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown EnvironmentJianxin Feng0https://orcid.org/0000-0002-3780-6863Jingze Zhang1https://orcid.org/0000-0002-2125-1330Geng Zhang2https://orcid.org/0000-0002-4499-556XShuang Xie3https://orcid.org/0000-0002-3033-7646Yuanming Ding4https://orcid.org/0000-0003-3280-4647Zhiguo Liu5https://orcid.org/0000-0003-0280-5040Communication and Network Key Laboratory, Dalian University, Dalian, ChinaCommunication and Network Key Laboratory, Dalian University, Dalian, ChinaCommunication and Network Key Laboratory, Dalian University, Dalian, ChinaCommunication and Network Key Laboratory, Dalian University, Dalian, ChinaCommunication and Network Key Laboratory, Dalian University, Dalian, ChinaCommunication and Network Key Laboratory, Dalian University, Dalian, ChinaThe application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can finally fly a safer path with high accuracy in an unknown environment with static or dynamic obstacles, and avoid local minima or solve local oscillation at the same time.https://ieeexplore.ieee.org/document/9615093/Artificial potential fieldMarkov chainUAV path planning |
| spellingShingle | Jianxin Feng Jingze Zhang Geng Zhang Shuang Xie Yuanming Ding Zhiguo Liu UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment IEEE Access Artificial potential field Markov chain UAV path planning |
| title | UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment |
| title_full | UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment |
| title_fullStr | UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment |
| title_full_unstemmed | UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment |
| title_short | UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment |
| title_sort | uav dynamic path planning based on obstacle position prediction in an unknown environment |
| topic | Artificial potential field Markov chain UAV path planning |
| url | https://ieeexplore.ieee.org/document/9615093/ |
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