工业协作机器人的动态速度缩放:一种以视线驱动的方法
Matteo Manzardo1, Federico Fraboni2, Sofia Morandini2
1Faculty of Engineering, Free University of Bozen/Bolzano, 39100, Bolzano, Italy.
Scientific reports
|March 10, 2026
概括
这项研究引入了一种新的人机无交互 (HRSI) 方法,通过根据操作员的视觉注意力和认知工作量动态调整机器人的行为来提高生产率和安全性. 可适应的机器人系统显著提高了协作效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 有效的人机无交互 (HRSI) 对工业机器人至关重要,需要将人类的心理过程整合到机器人的行动中.
- 现有的研究往往忽略了像视觉注意力这样的高频认知过程,主要关注的是像认知工作负载这样的低频过程.
- 在人机协作中优化安全,生产力和人体工程学需要对人类认知状态采取全面的方法.
研究的目的:
- 开发和验证一种基于高频 (视觉注意力) 和低频 (认知工作负载) 人类认知过程调整机器人行为的实时方法.
- 为了研究基于操作员视觉注意力的动态操纵器速度调制的影响.
- 评估机器人轨迹调整对优化操作员认知工作负载的影响.
主要方法:
- 开发了一个系统来监测操作员的目光,以评估视觉注意力,相应地动态调节操纵器速度.
- 机器人轨迹在实时调整,以优化操作员的认知工作量.
- 对26名参与者进行了实验验证,以评估系统的性能.
主要成果:
- 开发的算法使生产率提高了18%,认知工作量减少了5%,流性增加了10%,可用性,可靠性和接受度提高了5-9%.
- 利用高频认知过程 (视觉注意力) 显著改善了所有测量的指标.
- 机器人系统的适应性增加与增强的协作效率有积极的相关性.
结论:
- 拟议的方法有效地整合了高频和低频认知过程,以改善人机无交互 (HRSI).
- 基于操作员视觉注意力和认知工作负载的实时适应对于优化工业机器人系统至关重要.
- 未来的HRSI研究应该优先考虑利用高频认知过程,以加强人机协作.
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