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<p>Unmanned Aerial Vehicle (UAV) swarm path planning poses significant challenges, particularly in dynamic environments with complex obstacles. Traditional path-planning methods often encounter difficulties related to high dimensionality and obstacle density. This paper introduces a novel MA based on artificial intelligence, termed Improved Polar Lights Optimization (CCPLO). The CCPLO enhances path planning performance by integrating the Criss-Cross (CC) strategy with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Key improvements include a dynamic adjustment mechanism for search parameters, the implementation of parallel processing techniques, and refined crossover and normalization processes. The dynamic adjustment mechanism allows for real-time tuning of parameters, enhancing global search capabilities and minimizing the risk of local optima. Furthermore, parallel processing significantly boosts computational efficiency, especially in high-dimensional and complex scenarios. Experimental results demonstrate that the CCPLO algorithm outperforms existing algorithms in the CEC 2017 benchmark function test set. Specifically, in multi-obstacle and dynamic task environments, CCPLO effectively designs safer and more efficient paths, highlighting its strong potential for UAV swarm path planning.</p>

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Efficient multi-UAV path planning in dynamic and complex environments using hybrid polar lights optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月21日

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  • Ziyin Xu,
  • Zhilin Wang,
  • Rui Liu,
  • Chenliang Huang,
  • Yuxiang Shi,
  • Mingjing Wang,
  • Huiling Chen

摘要

Unmanned Aerial Vehicle (UAV) swarm path planning poses significant challenges, particularly in dynamic environments with complex obstacles. Traditional path-planning methods often encounter difficulties related to high dimensionality and obstacle density. This paper introduces a novel MA based on artificial intelligence, termed Improved Polar Lights Optimization (CCPLO). The CCPLO enhances path planning performance by integrating the Criss-Cross (CC) strategy with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Key improvements include a dynamic adjustment mechanism for search parameters, the implementation of parallel processing techniques, and refined crossover and normalization processes. The dynamic adjustment mechanism allows for real-time tuning of parameters, enhancing global search capabilities and minimizing the risk of local optima. Furthermore, parallel processing significantly boosts computational efficiency, especially in high-dimensional and complex scenarios. Experimental results demonstrate that the CCPLO algorithm outperforms existing algorithms in the CEC 2017 benchmark function test set. Specifically, in multi-obstacle and dynamic task environments, CCPLO effectively designs safer and more efficient paths, highlighting its strong potential for UAV swarm path planning.