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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Bibliographic Details
Published in:Neural computing & applications Vol. 37; no. 27; pp. 22353 - 22368
Main Authors: Wang, Ruo-Bin, Wang, Wei-Feng, Geng, Fang-Dong, Pan, Jeng-Shyang, Chu, Shu-Chuan, Xu, Lin
Format: Journal Article
Language:English
Published: London Springer London 01.09.2025
Springer Nature B.V
Subjects:
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
UAV path planning
Compact technique
Arithmetic optimization algorithm
Parallel technique
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary: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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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08983-2