在封闭的室内环境中动态机器人导航:释放感知子-Q学习融合
M Denesh Babu1, C Maheswari2, B Meenakshi Priya2
1Department of Electronics and Communication Engineering, The Kavery Engineering College, Salem 636453, India.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究引入了一种新的感知器-Q学习融合 (PQLF) 模型,用于在动态室内环境中的机器人导航. 与现有方法相比,PQLF模型显著降低了搬家成本和绕道百分比.
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
- 机器学习 机器学习
背景情况:
- 机器人在狭窄空间中的导航是具有挑战性的,因为动态障碍和依赖预先定义的地图.
- 现有的方法在不可预测的环境中与实时路径规划作斗争.
- 尽量减少移动成本和绕道对于实际的机器人导航至关重要.
研究的目的:
- 提出一种新的感知子-Q学习融合 (PQLF) 模型,用于高效的机器人导航.
- 为了提高机器人在动态,封闭的室内环境中的导航.
- 解决现有方法在处理动态障碍物和降低导航成本方面的局限性.
主要方法:
- 开发了一个感知器-Q学习融合 (PQLF) 模型,将感知器学习和Q学习结合起来.
- 利用机器人传感器在局部路径规划过程中动态确定障碍距离.
- 模拟动态机器人导航作为马尔科夫决策过程 (MDP),使用PQLF控制器作为代理.
主要成果:
- 采用PQLF模型,实现了1.1的流动成本降低.
- 拟议的模型导致绕道百分比为7.8%.
- 模拟结果显示,与现有的机器人导航方法相比,其性能优越.
结论:
- PQLF模型有效地提高了机器人在动态,封闭的室内环境中的导航.
- 感知学习和Q学习的融合为实时路径规划提供了强大的解决方案.
- 该模型通过降低移动成本和绕道来显著提高效率.
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