一个全球动态演进的雪除优化器,用于在空间障碍威胁下无人驾驶飞行器路径规划
Chenyu Liu1, Dongliang Zhang2, Wankai Li1
1College of Automation Engineering, Shanghai University of Electric Power, Shanghai, 200090, China.
Scientific reports
|December 2, 2024
概括
改进的全球动态进化除雪优化器 (GDSAO) 增强了全球优化和无人机 (UAV) 路径规划. 这种新的算法在复杂的3D环境中显示出卓越的可靠性和稳定性.
科学领域:
- 优化算法 优化算法
- 机器人和控制系统 机器人和控制系统
背景情况:
- 全球优化在复杂,高维空间中提出了挑战.
- 无人机 (UAV) 路径规划需要高效的算法来避免障碍物和满足约束.
研究的目的:
- 为全球优化提供改进的全球动态进化降雪优化器 (GDSAO).
- 应用GDSAO来解决有障碍威胁的无人机的3D路径规划问题.
主要方法:
- 引入了对Snow Ablation Optimizer (SAO) 的三个关键改进:多样化的人口初始化,自适应动态融雪比率和邻近维度搜索.
- 在30个十维CEC2017基准问题上评估了GDSAO.
- 测试了GDSAO用于无人机3D路径规划,确保了障碍物和拐角限制的满足.
主要成果:
- 在CEC 2017的基准问题上,GDSAO超过了几种领先的优化算法.
- 成功生成了无人机可行的路径,满足了所有避难障碍和拐角限制.
- 在优化任务中,与现有算法相比,证明了更高的可靠性和稳定性.
结论:
- 增强的GDSAO为全球优化问题提供了强大而有效的解决方案.
- GDSAO为复杂的无人机3D路径规划提供了可靠和稳定的方法.
- 提议的改进显著提高了雪除雪优化器的性能和适用性.
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