Three-Dimensional UAV Path Planning in Urban Environments Based on an Improved Parrot Optimization Algorithm

With the rapid development of unmanned aerial vehicle (UAV) technology, its applications in military and civilian fields are becoming increasingly widespread. However, realizing autonomous navigation of UAVs in complex environments still faces numerous challenges, especially the three-dimensional path planning problem. This paper proposes an improved parrot optimization algorithm (IPO) by introducing SPM chaotic mapping, adaptive switching factor, and hybrid Cauchy and Gaussian mutation strategies to enhance the algorithm's global search capability and convergence speed. The improved IPO is combined with the MATLAB simulation platform to construct a complete three-dimensional path-planning solution framework. Extensive simulation experiments demonstrate that, compared with the standard parrot optimization and other optimization algorithms, this algorithm significantly improves optimization accuracy, convergence speed, and path smoothness, showing good potential for engineering applications. The experimental results indicate that under the complex three-dimensional environment modeling of the Beijing International Studies University (BISU) campus, the improved IPO algorithm can quickly and accurately find the optimal collision-free path with minimal flight cost, which demonstrates excellent prospects for engineering applications.