移动机器人的障碍回避技术在自主的人机协作仓库环境中
Lucas C Sousa1, Yago M R Silva2, Vinícius B Schettino3
1Federal Center for Technological Education Celso Suckow da Fonseca, CEFET, Rio de Janeiro 20271-110, Brazil.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究介绍了一种集成的模糊逻辑和卷积神经网络 (CNN) 技术,用于移动机器人在人机器人协作 (HRC) 中避开障碍. 该方法在动态的工业环境中增强了自主导航和安全性.
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
- 控制系统 控制系统
背景情况:
- 人机协作 (HRC) 需要在动态环境中为移动机器人提供先进的导航.
- 在HRC任务中确保安全和生产力是一个重大挑战,需要智能避开障碍.
- 现有的方法可能难以实时适应复杂,不断变化的工作空间.
研究的目的:
- 开发和评估基于移动机器人的综合模糊逻辑和卷积神经网络 (CNN) 的障碍回避技术.
- 加强自主导航能力,确保HRC任务期间人员和设备的安全.
- 为实时适应和工业环境中安全交互提供强大的解决方案.
主要方法:
- 一种综合方法,将模糊逻辑规则和卷积神经网络 (CNN) 结合起来,用于机器人运动期间的物体检测.
- 使用机器人操作系统 (ROS) 和Gazebo进行基于模拟的测试,以测试避障系统.
- 实施控制系统,调整机器人的速度和曲率,以实现动态避障.
主要成果:
- 拟议的技术有效地检测物体,并使移动机器人的自主导航成为可能.
- 该系统在模拟的动态和复杂的工业环境中展示了实时适应能力.
- 成功避免了包括人类在内的静态和移动障碍物.
结论:
- 综合模糊逻辑和CNN方法为HRC的移动机器人避障提供了有效的解决方案.
- 开发的技术可以提高操作安全性,而不会影响协作机器人的生产力.
- 该框架为动态工业工作空间中安全的人机交互提供了可靠的方法.
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