3D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization Algorithm
Unmanned aerial vehicle (UAV) has been widely used in various fields such as agriculture, petroleum, military, meteorology, and geographic surveying. In actual flight, unmanned aerial vehicle needs to find the shortest path and avoid all threats. An improved particle swarm optimization algorithm com...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10891774/ |
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| author | Cunjie Li Qingli Zhao Canyi Che |
| author_facet | Cunjie Li Qingli Zhao Canyi Che |
| author_sort | Cunjie Li |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV) has been widely used in various fields such as agriculture, petroleum, military, meteorology, and geographic surveying. In actual flight, unmanned aerial vehicle needs to find the shortest path and avoid all threats. An improved particle swarm optimization algorithm combined with genetic algorithm method is presented in this paper to solve the path planning problem of UAV which easily falls into local optimization. The algorithm enhances early stage global search capability and later period local optimization capability compared to traditional particle swarm algorithm. To ensure that particles are distributed in key search areas of the environment, Gaussian distribution is used to initialize the particle distribution. Optimization ability can be further improved by the linear transformation operation on the well performing particles. Logistic function is employed to set the dynamic mutation probability. To improve the global search capability, random initialization operations are performed on particles with poor performance. Simulations in simple terrain and complex terrain environment are carried out to testify the feasibility and efficiency of the proposed improved algorithm. |
| format | Article |
| id | doaj-art-99de2a18506843e79dca8492fcb1426d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-99de2a18506843e79dca8492fcb1426d2025-08-20T03:16:25ZengIEEEIEEE Access2169-35362025-01-0113366373664610.1109/ACCESS.2025.3543175108917743D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization AlgorithmCunjie Li0https://orcid.org/0009-0006-7043-0358Qingli Zhao1https://orcid.org/0000-0001-5234-855XCanyi Che2https://orcid.org/0009-0004-1058-5206School of Science, Shandong Jianzhu University, Jinan, Shandong, ChinaSchool of Science, Shandong Jianzhu University, Jinan, Shandong, ChinaSchool of Science, Shandong Jianzhu University, Jinan, Shandong, ChinaUnmanned aerial vehicle (UAV) has been widely used in various fields such as agriculture, petroleum, military, meteorology, and geographic surveying. In actual flight, unmanned aerial vehicle needs to find the shortest path and avoid all threats. An improved particle swarm optimization algorithm combined with genetic algorithm method is presented in this paper to solve the path planning problem of UAV which easily falls into local optimization. The algorithm enhances early stage global search capability and later period local optimization capability compared to traditional particle swarm algorithm. To ensure that particles are distributed in key search areas of the environment, Gaussian distribution is used to initialize the particle distribution. Optimization ability can be further improved by the linear transformation operation on the well performing particles. Logistic function is employed to set the dynamic mutation probability. To improve the global search capability, random initialization operations are performed on particles with poor performance. Simulations in simple terrain and complex terrain environment are carried out to testify the feasibility and efficiency of the proposed improved algorithm.https://ieeexplore.ieee.org/document/10891774/3DpathplanningUAVparticle swarm optimizationgenetic algorithm |
| spellingShingle | Cunjie Li Qingli Zhao Canyi Che 3D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization Algorithm IEEE Access 3Dpathplanning UAV particle swarm optimization genetic algorithm |
| title | 3D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization Algorithm |
| title_full | 3D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization Algorithm |
| title_fullStr | 3D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization Algorithm |
| title_full_unstemmed | 3D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization Algorithm |
| title_short | 3D Flight Path Planning for UAV Based on Improved Particle Swarm Optimization Algorithm |
| title_sort | 3d flight path planning for uav based on improved particle swarm optimization algorithm |
| topic | 3Dpathplanning UAV particle swarm optimization genetic algorithm |
| url | https://ieeexplore.ieee.org/document/10891774/ |
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