使用移动机器人增强火算法进行优化路径规划
Mohd Nadhir Ab Wahab1, Amril Nazir2, Ashraf Khalil2
1School of Computer Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia.
PloS one
|August 12, 2024
概括
增强的火虫算法 (EFA) 通过优化无碰撞路线来改善移动机器人路径规划. 与标准的火算法相比,这种新方法提供了更好的导航效率和解决方案发现.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 路径规划对于移动机器人导航至关重要.
- 像标准的火算法 (FA) 这样的现有算法也有局限性.
- 超启发式技术越来越多地应用于复杂的优化问题.
研究的目的:
- 引入和评估用于移动机器人路径规划的增强火算法 (EFA).
- 通过算法修改来解决标准FA的限制.
- 在路径质量和避免碰撞方面,评估EFA与标准FA的性能.
主要方法:
- 实现了增强的火虫算法 (EFA),对α参数进行了线性减少.
- 在三个不同的地图上进行实验验证,每地图为EFA和标准FA进行20次运行.
- 评估指标包括无碰撞路径生成,路径距离,收速度和解决方案最佳性.
主要成果:
- 在所有测试的地图上,EFA在寻找更短的无碰撞路径方面取得了显著的改进.
- 与标准FA相比,具体的改进包括地图1的10.270%,地图2的0.371%,地图3的0.163%的增加.
- 欧洲经济论坛表现出更强的融合和发现最佳解决方案的能力.
结论:
- 增强的火虫算法 (EFA) 是有效的优化路径规划在移动机器人.
- 与标准的FA相比,EFA在导航效率和避免碰撞方面提供了卓越的性能.
- 这项研究为自主机器人导航系统提供了有希望的进步.
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