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<p>Three-dimensional (3D) path planning for Unmanned Aerial Vehicles (UAVs) constitutes a crucial element of autonomous flight technology within the Internet of Aerial Things (IoAT), with the objective of ensuring reliable and efficient route selection for UAVs operating in complex environments. To address the challenges associated with 3D path planning under intricate constraints, this study proposes an Enhanced Dung Beetle Optimizer (EDBO). Initially, the algorithm incorporates a fractional derivative approach to dynamically adjust the boundaries of the optimal search region, integrating the best-performing positions to refine boundary information. This enhancement endows the algorithm with memory characteristics and emphasizes the significance of global information exchange. Secondly, an optimal individual renewal mechanism is introduced, enabling the leading solution to guide other individuals across multiple search directions, thereby enhancing population diversity and mitigating the risk of premature convergence to local optima. Finally, a path refinement strategy based on quaternion interpolation is applied to ensure smoother flight trajectories. Simulation results comparing the performance of the EDBO algorithm against other optimization algorithms demonstrate that, in both simple and complex mountainous terrains, the EDBO achieves cost reductions of up to 27.27% and improves convergence speed by up to 14.33%. In six distinct flight scenarios involving threatening obstacles, the EDBO algorithm demonstrates average cost improvements of 13.80%, 6.93%, 4.27%, 11.40%, and 7.10%, respectively, when compared to alternative algorithms.</p>

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An enhanced dung beetle optimizer using fractional derivative for solving three-dimensional path planning problem

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Huangzhi Xia,
  • Yifen Ke,
  • Riwei Liao,
  • Huai Zhang

摘要

Three-dimensional (3D) path planning for Unmanned Aerial Vehicles (UAVs) constitutes a crucial element of autonomous flight technology within the Internet of Aerial Things (IoAT), with the objective of ensuring reliable and efficient route selection for UAVs operating in complex environments. To address the challenges associated with 3D path planning under intricate constraints, this study proposes an Enhanced Dung Beetle Optimizer (EDBO). Initially, the algorithm incorporates a fractional derivative approach to dynamically adjust the boundaries of the optimal search region, integrating the best-performing positions to refine boundary information. This enhancement endows the algorithm with memory characteristics and emphasizes the significance of global information exchange. Secondly, an optimal individual renewal mechanism is introduced, enabling the leading solution to guide other individuals across multiple search directions, thereby enhancing population diversity and mitigating the risk of premature convergence to local optima. Finally, a path refinement strategy based on quaternion interpolation is applied to ensure smoother flight trajectories. Simulation results comparing the performance of the EDBO algorithm against other optimization algorithms demonstrate that, in both simple and complex mountainous terrains, the EDBO achieves cost reductions of up to 27.27% and improves convergence speed by up to 14.33%. In six distinct flight scenarios involving threatening obstacles, the EDBO algorithm demonstrates average cost improvements of 13.80%, 6.93%, 4.27%, 11.40%, and 7.10%, respectively, when compared to alternative algorithms.