Graph-based semantic planning for adaptive human-robot-collaboration in assemble-to-order scenarios
Graph-based semantic planning for adaptive human-robot-collaboration in assemble-to-order scenarios
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DOI：
10.1109/ro-man57019.2023.10309425
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Ruidong Ma;Jingyu Chen;John Oyekan
中科院分区：
文献类型：
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作者：
Ruidong Ma;Jingyu Chen;John Oyekan
随着大规模定制制造需求的不断增长，按订单装配（ATO）已成为一种流行的生产策略。为了便于灵活和自动化的人-机器人-ATO协作系统，我们提出了一个从演示（LfD）框架的基础上2D视频在本文中。我们最初结合联合收割机的时间手部运动与空间的手对象的相互作用，以检测装配动作。因此，可以使用分类的动作序列来构造组装图。与以往的机器人任务规划研究相比，我们的基于图的语义规划器可以直接学习演示的任务结构，从而产生更详细的辅助机器人动作，以实现更有效的协作。我们通过将其应用于现实世界的ATO问题来验证我们的方法。结果表明，我们提出的系统可以产生行动自适应响应不同的人类动作序列，以及指导人类组装时，机器人不参与。我们的方法也显示了不可见的人类动作序列的普遍性。
Assemble-to-Order (ATO) has become a popular production strategy for the increasing demand for mass-customized manufacturing. In order to facilitate a flexible and automated Human-Robot-Collaboration system for ATO, we propose a Learning from Demonstration (LfD) framework based on 2D videos in this paper. We initially combine temporal hand motions with spatial hand-object interactions to detect assembly actions. Therefore, an assembly graph can be constructed using classified action sequences. Compared to previous studies on task planning for robots, our graph-based semantic planner can directly learn the demonstrated task structure and thus produce more detailed assistive robot actions for more effective collaboration. We validate our approach by applying it to a real-world ATO problem. The results demonstrated that our proposed system can produce actions adaptively in response to varying human action sequences, as well as guide human assembly when the robot is not involved. Our approach also shows generalizability to unseen human action sequences.