Three-Dimensional UAV Trajectory Planning Based on Improved Sparrow Search Algorithm
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| Yayımlandı: | Symmetry vol. 17, no. 12 (2025), p. 2071-2096 |
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| Yazar: | |
| Diğer Yazarlar: | , , , |
| Baskı/Yayın Bilgisi: |
MDPI AG
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| Konular: | |
| Online Erişim: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2073-8994 | ||
| 024 | 7 | |a 10.3390/sym17122071 |2 doi | |
| 035 | |a 3286357289 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231635 |2 nlm | ||
| 100 | 1 | |a Yang, Yong |u School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China | |
| 245 | 1 | |a Three-Dimensional UAV Trajectory Planning Based on Improved Sparrow Search Algorithm | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Whether an unmanned aerial vehicle (UAV) can complete its mission successfully is determined by trajectory planning. Reasonable and efficient UAV trajectory planning in 3D environments is a complex global optimization problem, in which numerous constraints need to be considered carefully, including mountainous terrain, obstacles, no-fly zones, safety altitude, smoothness, flight distance, and so on. Generally speaking, symmetry characteristics from the starting point to the endpoint can be concluded from the potential spatial multiple trajectories. Aiming at the deficiencies of the Sparrow Search Algorithm (SSA) in 3D symmetric trajectory planning such as population diversity and local optimization, the sine–cosine function and the Lévy flight strategy are combined, and the Improved Sparrow Search Algorithm (ISSA) is proposed, which can find a better solution in a shorter time by dynamically adjusting the search step size and increasing the occasional large step jumps so as to increase the symmetry balance of the global search and the local development. In order to verify the effectiveness of the improved algorithm, ISSA is simulated and compared with the Sparrow Search Algorithm (SSA), Particle Swarm Algorithm (PSO), Gray Wolf Algorithm (GWO) and Whale Optimization Algorithm (WOA) in the same environment. The results show that the ISSA algorithm outperforms the comparison algorithms in key indexes such as convergence speed, path cost, obstacle avoidance safety, and path smoothness, and can meet the requirement of obtaining a higher-quality flight path in a shorter number of iterations. | |
| 653 | |a Aircraft accidents & safety | ||
| 653 | |a Simulation | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Smoothness | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Global optimization | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Altitude | ||
| 653 | |a Search algorithms | ||
| 653 | |a Flight | ||
| 653 | |a Local optimization | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Trajectory planning | ||
| 653 | |a Symmetry | ||
| 653 | |a Obstacle avoidance | ||
| 700 | 1 | |a Sun, Li |u School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China | |
| 700 | 1 | |a Fu Yujie |u School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China | |
| 700 | 1 | |a Feng Weiqi |u School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China | |
| 700 | 1 | |a Xu Kaijun |u School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, China | |
| 773 | 0 | |t Symmetry |g vol. 17, no. 12 (2025), p. 2071-2096 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286357289/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286357289/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286357289/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |