A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning
Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/10/633 |
| _version_ | 1827721142103703552 |
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| author | Tao Xiong Hao Li Kai Ding Haoting Liu Qing Li |
| author_facet | Tao Xiong Hao Li Kai Ding Haoting Liu Qing Li |
| author_sort | Tao Xiong |
| collection | DOAJ |
| description | Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO. |
| first_indexed | 2024-03-10T21:18:23Z |
| format | Article |
| id | doaj.art-2feff350175f482db8e2c61fade367cf |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T21:18:23Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-2feff350175f482db8e2c61fade367cf2023-11-19T16:15:42ZengMDPI AGDrones2504-446X2023-10-0171063310.3390/drones7100633A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path PlanningTao Xiong0Hao Li1Kai Ding2Haoting Liu3Qing Li4Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, ChinaScience and Technology on Near-Surface Detection Laboratory, Wuxi 214035, ChinaScience and Technology on Near-Surface Detection Laboratory, Wuxi 214035, ChinaShunde Innovation School, University of Science and Technology Beijing, Foshan 528399, ChinaShunde Innovation School, University of Science and Technology Beijing, Foshan 528399, ChinaGiven the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO.https://www.mdpi.com/2504-446X/7/10/633drone3D path planningimproved symbiotic organisms searchsine–cosine particle swarm optimizationdisaster relief |
| spellingShingle | Tao Xiong Hao Li Kai Ding Haoting Liu Qing Li A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning Drones drone 3D path planning improved symbiotic organisms search sine–cosine particle swarm optimization disaster relief |
| title | A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning |
| title_full | A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning |
| title_fullStr | A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning |
| title_full_unstemmed | A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning |
| title_short | A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning |
| title_sort | hybrid improved symbiotic organisms search and sine cosine particle swarm optimization method for drone 3d path planning |
| topic | drone 3D path planning improved symbiotic organisms search sine–cosine particle swarm optimization disaster relief |
| url | https://www.mdpi.com/2504-446X/7/10/633 |
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