UAV Path Planning Based on Ensembled Golden Sine-Dung Beetle Optimization Algorithm
Jingping Xu, Xin Zhang, Wenjie Wang, Zhenyang Wei · 2025
Finding a low-cost flight path that avoids obstacles is an important research area in UAV. In complex 3D environments, many algorithms are unsatisfactory for path planning. An Ensembled golden sine-dung beetle optimization algorithm (EGSADBO) is proposed to improve the performance of the UAV. The dung beetle population is first initialized using the set of good points to obtain a more comprehensive coverage. Second, the golden sine operator is introduced to improve the population update position and enhance the global search capability of the algorithm. Finally, new boundary control approaches are proposed to strengthen the algorithm’s local search performance. EGSADBO is applied to UAV 3D map path planning to verify its performance, and the results show that EGSADBO reduces the cost consumption by $8.0 \%-34.9 \%$ compared to other algorithms.