在现代物流中,盒装盒插入任务代表了广泛的包装应用,并且由于在插入期间对对象变形进行建模的挑战,因此难以使顺应性对象插入自动化。使用从演示中学习(LfD)范式,它们经常用于机器人技术中,以促进从人类到机器人的技能转移,可以是难以数学建模的复杂任务的一种解决方案。为了自动执行打包应用程序的盒中插入任务,本研究利用LfD技术。拟议的框架分为三个阶段。首先,在初始阶段使用主从遥控机器人系统来触觉演示插入任务。然后,学习阶段涉及使用概率方法识别演示轨迹中的趋势,在这种情况下,高斯混合回归。在第三阶段,插入任务是广义的,和机器人使用重心插值调整到任何对象位置。这种方法是新颖的,因为它通过利用盒子的自然顺应性来解决紧密插入问题,即使使用位置控制的机器人也可以完成任务。为了确定策略是否可概括和可重复,进行了实验验证。
In modern logistics, the box-in-box insertion task is representative of a wide range of packaging applications, and automating compliant object insertion is difficult due to challenges in modelling the object deformation during insertion. Using Learning from Demonstration (LfD) paradigms, which are frequently used in robotics to facilitate skill transfer from humans to robots, can be one solution for complex tasks that are difficult to mathematically model. In order to automate the box-in-box insertion task for packaging applications, this study makes use of LfD techniques. The proposed framework has three phases. Firstly, a master-slave teleoperated robot system is used in the initial phase to haptically demonstrate the insertion task. Then, the learning phase involves identifying trends in the demonstrated trajectories using probabilistic methods, in this case, Gaussian Mixture Regression. In the third phase, the insertion task is generalised, and the robot adjusts to any object position using barycentric interpolation. This method is novel because it tackles tight insertion by taking advantage of the boxes\' natural compliance, making it possible to complete the task even with a position-controlled robot. To determine whether the strategy is generalisable and repeatable, experimental validation was carried out.