Efficient multi-UAV path planning in dynamic and complex environments using hybrid polar lights optimization
Abstract 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 b...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00139-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract 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. |
|---|---|
| ISSN: | 1319-1578 2213-1248 |