使用OPTION-A*-DQN算法优化海上风电场的无人机检查路径.
Meiqing Xu1, Chao Deng1, Xiangyu Hu1
1School of Computer Science and Engineering, Guangdong Ocean University, Guangdong, China.
PloS one
|November 24, 2025
概括
本研究引入了一种新的路径规划方法,用于在海上风电场的无人机 (UAV) 检查,提高效率和任务完成率. 这种新的方法优化了路线,减少了旅行距离,并加快了模拟,以更好地维护风电场.
科学领域:
- 机器人和自动化 机器人和自动化
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
背景情况:
- 海上风电场检查由于冗余的路径和错过的任务而面临效率低下.
- 目前的检查方法需要优化无人机 (UAV) 操作.
- 路径规划对于有效和全面的海上风电场监测至关重要.
研究的目的:
- 为无人机在海上风电场检查中开发一种新的智能路径规划方法.
- 解决当前检查协议中路径冗余和任务遗漏等低效的问题.
- 提高海上风电场维护操作的整体有效性和效率.
主要方法:
- 建立了一个四维约束模型,包括风速,充电,车队大小和避开障碍物.
- 开发了OPTION-A*-DQN混合算法,将A*搜索与深度强化学习 (DRL) 结合起来.
- 利用改进的K-Means算法,在多UAV协作中实现高效的拓分区.
主要成果:
- 与现有方法相比,实现了比现有方法高10%的任务完成率.
- 通过优化路线规划,路径距离减少了14.9%.
- 演示了20%更快的模拟时间,表明计算效率有所提高.
结论:
- 拟议的路径规划方法显著提高了海上风电场检查的效率.
- 多限制优化和智能调度的整合为无人机运营提供了强大的解决方案.
- 这项研究促进了关键基础设施监控中自主系统的智能路径规划.
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