Using Bayesian Filtering to Localize Flexible Materials During Manipulation
Using Bayesian Filtering to Localize Flexible Materials During Manipulation
复制标题
在操作过程中使用贝叶斯过滤来定位柔性材料
DOI：
10.1109/tro.2011.2139150
复制
发表时间：
2011
影响因子：
7.8
通讯作者：
Joel Pfeiffer
中科院分区：
文献类型：
--
作者：
Robert W. Platt;Frank Permenter;Joel Pfeiffer
定位和操作的功能，如按钮，卡扣，或嵌在织物和其他柔性材料的金属片是一个困难的机器人问题。过于依赖在接触材料之前发生的传感和定位的方法可能会失败，因为当机器人实际接触时，柔性材料可以移动。本文通过实验探索了使用本体感受和基于负载的触觉信息来定位机器人操作过程中嵌入柔性材料中的特征的可能性。在我们的实验中，Robonaut 2，一个拥有类似人类的手和手臂的机器人，使用粒子滤波来定位基于本体感受和触觉测量的特征。我们的主要贡献是提出了一种与柔性材料相互作用的方法，该方法通过迫使材料以可重复的方式遵守来减少相互作用的状态空间。测量结果与在训练阶段期间创建的“触觉图”相匹配，该“触觉图”将预期测量结果描述为状态的低维函数。我们评估定位性能时，单独使用本体感受信息和触觉数据时，也可。这两种类型的测量显示包含互补的信息。我们发现，触觉测量模型是至关重要的定位性能，并提出了一系列的模型，提供越来越好的精度。最后，本文探讨了本地化方法的背景下，两个灵活的材料插入的任务，是相关的制造应用程序。
Localization and manipulation of features such as buttons, snaps, or grommets embedded in fabrics and other flexible materials is a difficult robotics problem. Approaches that rely too much on sensing and localization that occurs before touching the material are likely to fail because the flexible material can move when the robot actually makes contact. This paper experimentally explores the possibility to use proprioceptive and load-based tactile information to localize features embedded in flexible materials during robot manipulation. In our experiments, Robonaut 2, a robot with human-like hands and arms, uses particle filtering to localize features based on proprioceptive and tactile measurements. Our main contribution is to propose a method to interact with flexible materials that reduces the state space of the interaction by forcing the material to comply in repeatable ways. Measurements are matched to a “haptic map,” which is created during a training phase, that describes expected measurements as a low-dimensional function of state. We evaluate localization performance when using proprioceptive information alone and when tactile data are also available. The two types of measurements are shown to contain complementary information. We find that the tactile measurement model is critical to localization performance and propose a series of models that offer increasingly better accuracy. Finally, this paper explores the localization approach in the context of two flexible material insertion tasks that are relevant to manufacturing applications.