Three-Dimensional Path Planning for Unmanned Aerial Vehicles Based on Hybrid Multi-Strategy Dung Beetle Optimization Algorithm

Hongmei Fei, Ruru Liu, Leilei Dong, Zhaohui Du, Xuening Liu, Tao Luo and Jie Zhou ()
Additional contact information
Hongmei Fei: College of Information Science and Technology, Shihezi University, Shihezi 832000, China
Ruru Liu: College of Information Science and Technology, Shihezi University, Shihezi 832000, China
Leilei Dong: College of Information Science and Technology, Shihezi University, Shihezi 832000, China
Zhaohui Du: College of Information Science and Technology, Shihezi University, Shihezi 832000, China
Xuening Liu: College of Information Science and Technology, Shihezi University, Shihezi 832000, China
Tao Luo: College of Information Science and Technology, Shihezi University, Shihezi 832000, China
Jie Zhou: College of Information Science and Technology, Shihezi University, Shihezi 832000, China

Agriculture, 2025, vol. 15, issue 11, 1-33

Abstract: In complex environments, three-dimensional path planning for agricultural UAVs involves the comprehensive consideration of multiple factors, including obstacle avoidance, path optimization, and computational efficiency, which significantly complicates the achievement of safe and efficient flight. As environmental complexity increases, the search space expands exponentially, thereby making the problem more challenging to solve and categorizing it as an NP-hard problem. To obtain an optimal or near-optimal path within this vast search space, it is essential to balance the path length, safety, and computational cost. This paper proposes a novel UAV path planning method based on the Hybrid Multi-Strategy Dung Beetle Optimization Algorithm (HMSDBO), which effectively reduces path length and improves path smoothness. First, a new Latin hypercube sampling strategy is introduced to significantly enhance the population diversity and improve the global search capabilities. Furthermore, an innovative golden sine strategy is proposed to greatly enhance the algorithm’s robustness. Lastly, a new hybrid adaptive weighting strategy is employed to improve the algorithm’s stability and reliability. To validate the effectiveness of HMSDBO, this study compares its performance with that of the Adaptive Chaotic Gray Wolf Optimization Algorithm (ACGWO), Primitive Dung Beetle Optimization Algorithm (DBO), Whale Optimization Algorithm (WOA), Crayfish Optimization Algorithm (COA), and Hyper-Heuristic Whale Optimization Algorithm (HHWOA) in complex agricultural UAV environments. Experimental results show that the path lengths calculated by HMSDBO are reduced by 21.3%, 7.88%, 19.95%, 8.09%, and 4.2%, respectively, compared to the aforementioned algorithms. This reduction significantly enhances both the optimization effectiveness and the smoothness of three-dimensional path planning for agricultural UAVs.

Keywords: unmanned aerial vehicle; route planning; dung beetle optimization algorithm; hyper-heuristic whale optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/11/1156/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/11/1156/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:11:p:1156-:d:1666258

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().