基于数字双胞胎的移动机器人动力学模型的参数补偿方法和控制策略研究
Renjun Li1, Xiaoyu Shang1, Yang Wang2
1School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了对检查机器人的可视化监控系统,将其位置精度提高18%,以便在电池工厂等高风险环境中进行更安全的操作.
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
- 工业安全 工业安全 工业安全
背景情况:
- 检查机器人对于高风险行业 (电力,石化,电池工厂) 的安全至关重要.
- 局限的位置精度阻碍了当前检查机器人的广泛应用.
- 准确的动力学模型和控制系统是不可靠的导航需要在非结构化的表面.
研究的目的:
- 为智能工厂开发可视化监控系统框架.
- 提高移动机器人的位置精度和检查效率.
- 解决工业机器人的虚拟现实集成和实时数据管理方面的挑战.
主要方法:
- 利用虚拟引擎和数字双胞胎创建一个可视化监控系统框架.
- 开发了一种基于神经网络的补偿技术,用于户外移动机器人的非线性动态模型参数.
- 实施并试验了一种在线控制和监控的物理原型.
主要成果:
- 拟议的系统证明了户外移动机器人的有效在线控制和监控,具有高实时性能和可视化.
- 精确的动力学模型在障碍物导航过程中将机器人的位置精度提高了18%.
- 视觉化系统可以在危险环境中进行全面的,多方法的实时检查.
结论:
- 开发的可视化监控系统显著提高了机器人的位置精度和检查效率.
- 该系统确保在高风险环境中安全稳定运行,例如新能源电池工厂.
- 该框架为智能制造中的先进,数据驱动的机器人检查提供了基础.
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