使用障碍回避的故障点搜索对机械进行诊断 机器人使用层次的模糊逻辑控制
Rui Mu1, Ryojun Ikeura1, Hongtao Xue2
1Graduate School of Engineering, Mie University, Tsu 514-8507, Japan.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
自主诊断机器人提高了工厂的安全性和效率. 本研究介绍了用于机器人的模糊逻辑导航算法,确保在工业环境中检测故障时安全避开障碍.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 工业工程 工业工程 工业工程
背景情况:
- 现代工厂需要先进的故障检测来持续运行.
- 手动检查耗时,需要自主诊断机器人.
- 现有的机器人在动态工厂环境中面临安全限制.
研究的目的:
- 为诊断机器人提出一个基于模糊逻辑的层次导航和避障算法.
- 提高工厂自主故障检测的安全性和效率.
- 为了解决当前诊断机器人导航系统的局限性.
主要方法:
- 开发了一个使用零顺序Takagi-Sugeno模糊控制的层次模糊逻辑算法.
- 集成的子函数用于导航,静态和动态避难障碍.
- 引入了一个动态安全边界概念,使用规范的突破水平和相对速度方向来避免障碍.
主要成果:
- 拟议的算法成功引导诊断机器人距离故障点30厘米之内.
- 确保避免与静态设备和动态障碍物发生碰撞.
- 通过模拟在故障点搜索过程中证明了增强的完整性和安全性.
结论:
- 层次的模糊逻辑算法为自主诊断机器人导航提供了安全有效的解决方案.
- 动态安全边界和多目标规划提高了复杂工业环境中的机器人性能.
- 这种方法提高了连续运行的机器故障检测的整体安全性和效率.
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