Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method
Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant co...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3652 |
| _version_ | 1827666677764980736 |
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| author | Jun Fu Teng Lv Bao Li |
| author_facet | Jun Fu Teng Lv Bao Li |
| author_sort | Jun Fu |
| collection | DOAJ |
| description | Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm. |
| first_indexed | 2024-03-10T01:55:04Z |
| format | Article |
| id | doaj.art-85b682af8dd8445c9a09a2bb7c03c410 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:55:04Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-85b682af8dd8445c9a09a2bb7c03c4102023-11-23T12:58:52ZengMDPI AGSensors1424-82202022-05-012210365210.3390/s22103652Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle MethodJun Fu0Teng Lv1Bao Li2Department of Navigation Engineering, Naval University of Engineering, Wuhan 430000, ChinaDepartment of Navigation Engineering, Naval University of Engineering, Wuhan 430000, ChinaDepartment of Navigation Engineering, Naval University of Engineering, Wuhan 430000, ChinaNavigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm.https://www.mdpi.com/1424-8220/22/10/3652underwaterpath planningartificial potential fieldant colony algorithmvelocity obstacle method |
| spellingShingle | Jun Fu Teng Lv Bao Li Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method Sensors underwater path planning artificial potential field ant colony algorithm velocity obstacle method |
| title | Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method |
| title_full | Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method |
| title_fullStr | Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method |
| title_full_unstemmed | Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method |
| title_short | Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method |
| title_sort | underwater submarine path planning based on artificial potential field ant colony algorithm and velocity obstacle method |
| topic | underwater path planning artificial potential field ant colony algorithm velocity obstacle method |
| url | https://www.mdpi.com/1424-8220/22/10/3652 |
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