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基于点云模型的复杂表面喷涂的自主轨迹规划
Saul Nieto Bastida1, Chyi-Yeu Lin1,2
1Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了一种用于自主机器人轨迹生成的算法,用于使用3D点云数据喷涂复杂表面. 它简化了各种生产线的路径规划,提高了效率和质量.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 制造业自动化 制造业自动化
背景情况:
- 传统的机器人编程任务,如喷涂涂料是复杂和耗时的.
- 由于设置时间,当前的方法对低体积,高变化生产线效率低下.
- 表面的复杂性和需要熟练操作人员的需求在自动喷涂中带来了重大挑战.
研究的目的:
- 开发一种自主机器人轨迹生成算法,用于喷涂复杂表面.
- 在可变的生产环境中克服传统编程方法的局限性.
- 为了使具有复杂几何形状的物体能够高效,高质量的喷涂.
主要方法:
- 使用对象的3D点云数据作为输入.
- 使用预定义的球形网格来组织几何属性.
- 实现自动路径规划和区域提取的算法.
- 开发一个图形用户界面 (GUI) 用于参数定义和可视化.
- 集成一个3D传感器,实时定位工件和调整轨迹.
主要成果:
- 成功生成了自主机器人轨迹,用于喷涂复杂表面.
- 在不同工件的模拟中验证了该方法.
- 在真实机器人上证明了该方法的有效性,用于动作执行.
- 图形界面方便可视化点云,轨迹和模拟的油漆质量.
结论:
- 拟议的算法有效地自动化了用于喷涂复杂物体的机器人路径规划.
- 这种方法为低产量和高变化制造线提供了灵活和高效的解决方案.
- 整合3D传感和自主路径规划可以提高机器人喷涂的精度和质量.
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