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<p>To address the complex flight situation of UAV path planning, we model the drone’s flight trajectory. In order to reduce the cost of the drone’s minimum flight path, obstacles, flight altitude, smoothness, and wind speed, we propose an improved giant armadillo optimization algorithm (IGAO). The algorithm first uses Singer chaotic mapping to initialize the population, increases the diversity of the population, makes the population distribution more uniform, and accelerates the convergence speed. Secondly, we combine the subtraction average algorithm with the division idea in arithmetic optimization algorithm, and improve the exploration stage by perturbing near the optimal value to escape from the local optimal solution. Then, IGAO was experimented and analyzed with GAO, GWO, WOA, DBO, HHO, and PSO in 12 basic test functions, and applied to UAV path planning and two engineering design problems. The experimental results show that the proposed IGAO algorithm has significant advantages in optimizing accuracy and efficiency, proving its effectiveness and feasibility.</p>

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UAV path planning based on improved giant armadillo optimisation algorithm

  • 期刊论文

  • 发表日期：

    2025年12月18日

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  • Yangyang Tian,
  • Zhe Li,
  • Liang Jiang,
  • Huanlong Zhang,
  • Yanfeng Wang

摘要

To address the complex flight situation of UAV path planning, we model the drone’s flight trajectory. In order to reduce the cost of the drone’s minimum flight path, obstacles, flight altitude, smoothness, and wind speed, we propose an improved giant armadillo optimization algorithm (IGAO). The algorithm first uses Singer chaotic mapping to initialize the population, increases the diversity of the population, makes the population distribution more uniform, and accelerates the convergence speed. Secondly, we combine the subtraction average algorithm with the division idea in arithmetic optimization algorithm, and improve the exploration stage by perturbing near the optimal value to escape from the local optimal solution. Then, IGAO was experimented and analyzed with GAO, GWO, WOA, DBO, HHO, and PSO in 12 basic test functions, and applied to UAV path planning and two engineering design problems. The experimental results show that the proposed IGAO algorithm has significant advantages in optimizing accuracy and efficiency, proving its effectiveness and feasibility.