通过机器学习优化机器人接过程的智能制造范式
Javubar Sathick1, A Abdul Azeez Khan2
1Department of Computer Applications, B.S. Abdur Rahman Crescent Institute of Science & Technology, Chennai, 600048, India.
Scientific reports
|November 29, 2025
概括
本研究介绍了机器人接过程优化 (RL-RWPO) 的强化学习,以提高机器人接的准确性和效率. 智能制造方法在工业应用中显著提高了接质量,速度和轨迹精度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 制造业 工程 制造工程
- 人工智能的人工智能
背景情况:
- 机器人接对于整个行业的结构完整性至关重要.
- 传统方法在工件位置和轨迹准确性方面存在困难,限制了最佳的接结果.
- 需要先进的优化方法来解决这些局限性.
研究的目的:
- 通过使用强化学习来优化机器人接工艺 (RL-RWPO) 的智能制造范式.
- 提高机器人接过程的精度,效率和整体质量.
- 整合基于视觉和基于CAD的方法,以全面了解工件的放置和排列.
主要方法:
- 开发了RL-RWPO框架,结合了基于视觉和基于CAD的方法.
- 实施3D校准和工件放置技术,以提高精度.
- 利用强化学习用于机器人接操作中的轨迹优化.
主要成果:
- 实现了93.27%的平均接接质.
- 达到了50.51mm/s的接速度和5.25mm的接透深度.
- 证明轨迹精度为3.04毫米,接效率为91.52%.
结论:
- RL-RWPO方法显著提高了机器人接的有效性和卓越性.
- 综合方法通过3D校准和工件放置来提高精度.
- 该框架为优化各种工业部门的机器人接提供了可行的解决方案.
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