Scholarlink

<p>Multi-UAV cooperative path planning in complex urban environments faces critical challenges in balancing exploration efficiency, convergence precision, and computational practicality. This study proposes a Hybrid Direction-Assisted Polar Light Optimization (HDPLO) algorithm that integrates three key innovations: directional heuristic mechanisms for intelligent search space expansion, adaptive crossover strategies for diversity maintenance, and CMA-ES integration for high-precision local refinement. Experimental validation using CEC2017 benchmarks and realistic multi-UAV scenarios (obstacle densities 7.71%-77.11%) demonstrates significant improvements: 38.9% better solution quality on benchmark functions, 45.7% faster convergence, 15.2% reduced path costs in UAV planning, and 11.1%-38.5% fewer turning maneuvers compared to state-of-the-art algorithms. Statistical analysis confirms performance reliability (p&lt;0.001), while ablation studies verify each component’s essential contribution. These findings establish HDPLO as a robust solution for intelligent UAV systems requiring high reliability and efficiency in real-world deployments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Intelligent multi-UAV path planning in complex environments: a hybrid optimization approach with direction-assisted search and adaptive refinement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月5日

查看全文
  • Tiancheng Jin,
  • Yujie Zhu

摘要

Multi-UAV cooperative path planning in complex urban environments faces critical challenges in balancing exploration efficiency, convergence precision, and computational practicality. This study proposes a Hybrid Direction-Assisted Polar Light Optimization (HDPLO) algorithm that integrates three key innovations: directional heuristic mechanisms for intelligent search space expansion, adaptive crossover strategies for diversity maintenance, and CMA-ES integration for high-precision local refinement. Experimental validation using CEC2017 benchmarks and realistic multi-UAV scenarios (obstacle densities 7.71%-77.11%) demonstrates significant improvements: 38.9% better solution quality on benchmark functions, 45.7% faster convergence, 15.2% reduced path costs in UAV planning, and 11.1%-38.5% fewer turning maneuvers compared to state-of-the-art algorithms. Statistical analysis confirms performance reliability (p<0.001), while ablation studies verify each component’s essential contribution. These findings establish HDPLO as a robust solution for intelligent UAV systems requiring high reliability and efficiency in real-world deployments.