UAV path planning in mountain areas based on a hybrid parallel compact arithmetic optimization algorithm
Unmanned Aerial Vehicle (UAV) path planning is one of the core components of its entire autonomous control system. The main challenge lies in efficiently obtaining an optimal flight route in complex environments, especially in mountain areas. To address this, we propose a novel version of arithmetic...
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| Published in: | Neural computing & applications Vol. 37; no. 27; pp. 22353 - 22368 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer London
01.09.2025
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | Unmanned Aerial Vehicle (UAV) path planning is one of the core components of its entire autonomous control system. The main challenge lies in efficiently obtaining an optimal flight route in complex environments, especially in mountain areas. To address this, we propose a novel version of arithmetic optimization algorithm (AOA), named parallel and compact AOA (PCAOA). In PCAOA, the compact technique can save the memory of UAV and shorten the calculation time, and the parallel technique can quicken the convergence speed and improve the solution accuracy. In addition, the flight path generated by PCAOA is smoothed with cubic B-spline curves, making the path suitable for a UAV. The performance of PCAOA is demonstrated on 23 benchmark functions. Experimental results show that PCAOA achieves competitive results. Finally, the simulation studies are conducted to verify that PCAOA can successfully acquire a feasible and effective route in different mountain areas. |
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| AbstractList | Unmanned Aerial Vehicle (UAV) path planning is one of the core components of its entire autonomous control system. The main challenge lies in efficiently obtaining an optimal flight route in complex environments, especially in mountain areas. To address this, we propose a novel version of arithmetic optimization algorithm (AOA), named parallel and compact AOA (PCAOA). In PCAOA, the compact technique can save the memory of UAV and shorten the calculation time, and the parallel technique can quicken the convergence speed and improve the solution accuracy. In addition, the flight path generated by PCAOA is smoothed with cubic B-spline curves, making the path suitable for a UAV. The performance of PCAOA is demonstrated on 23 benchmark functions. Experimental results show that PCAOA achieves competitive results. Finally, the simulation studies are conducted to verify that PCAOA can successfully acquire a feasible and effective route in different mountain areas. |
| Author | Geng, Fang-Dong Wang, Wei-Feng Chu, Shu-Chuan Wang, Ruo-Bin Xu, Lin Pan, Jeng-Shyang |
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| SubjectTerms | Algorithms Arithmetic Artificial Intelligence B spline functions Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Efficiency Flight Heuristic Image Processing and Computer Vision Mathematical analysis Mountainous areas Mountains Optimization Optimization algorithms Path planning Performance evaluation Planning Probability and Statistics in Computer Science S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Unmanned aerial vehicles |
| Title | UAV path planning in mountain areas based on a hybrid parallel compact arithmetic optimization algorithm |
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