An Adaptable Robot Vision System Performing Manipulation Actions With Flexible Objects
An Adaptable Robot Vision System Performing Manipulation Actions With Flexible Objects
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DOI：
10.1109/tase.2014.2320157
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发表时间：
2014-05
影响因子：
5.6
通讯作者：
L. Bodenhagen;A. R. Fugl;Andreas Jordt;M. Willatzen;K. A. Andersen;Martin M. Olsen;R. Koch;H. G. Petersen;N. Krüger
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文献类型：
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作者：
L. Bodenhagen;A. R. Fugl;Andreas Jordt;M. Willatzen;K. A. Andersen;Martin M. Olsen;R. Koch;H. G. Petersen;N. Krüger
本文描述了一个自适应系统，它能够执行操作操作（如钉孔或铺设行动）与灵活的对象。由于这些对象在执行动作期间很容易显著地改变它们的形状，传统的策略，例如用于解决路径规划问题的策略，通常不适用。因此，需要将视觉跟踪和形状重建与材料及其变形的物理建模以及动作学习技术相结合。所有这些不同的子模块已被集成到一个演示平台，实时运行。模拟已被用于引导最佳动作的学习，随后通过现实世界的执行进行改进。为了获得可重复的结果，我们证明了这一点，为铸造硅胶测试对象的规则形状。从业人员注意-这项工作的目的是促进建立基于机器人的自动化微妙的处理由统一的材料组成的柔性物体。作为例子，我们已经考虑了如何最佳地操纵柔性物体通过一个洞，而不会发生碰撞，以及如何将柔性物体放置在一个平面上，在物体中引入最小的内应力。考虑到物体的材料特性，我们已经在这两个应用程序中演示了如何在最小的人为干预要求下对系统进行编程。我们的系统不应是一个缺乏灵活性的集成系统，而应被视为一个已被证明在接近工业条件下工作的新技术库。作为一个相当基本但必要的部分，我们提供了一种技术，用于在传递时确定对象的形状，例如，在被搬运之前的传送带。适用于被操纵对象的主要技术是：在操纵过程中实时跟踪柔性对象的方法，基于模型的离线预测抓取柔性对象的静态变形的方法，以及最后，基于模拟和现实执行优化特定任务的方法。
This paper describes an adaptable system which is able to perform manipulation operations (such as Peg-in-Hole or Laying-Down actions) with flexible objects. As such objects easily change their shape significantly during the execution of an action, traditional strategies, e.g, for solve path-planning problems, are often not applicable. It is therefore required to integrate visual tracking and shape reconstruction with a physical modeling of the materials and their deformations as well as action learning techniques. All these different submodules have been integrated into a demonstration platform, operating in real-time. Simulations have been used to bootstrap the learning of optimal actions, which are subsequently improved through real-world executions. To achieve reproducible results, we demonstrate this for casted silicone test objects of regular shape. Note to Practitioners - The aim of this work was to facilitate the setup of robot-based automation of delicate handling of flexible objects consisting of a uniform material. As examples, we have considered how to optimally maneuver flexible objects through a hole without colliding and how to place flexible objects on a flat surface with minimal introduction of internal stresses in the object. Given the material properties of the object, we have demonstrated in these two applications how the system can be programmed with minimal requirements of human intervention. Rather than being an integrated system with the drawbacks in terms of lacking flexibility, our system should be viewed as a library of new technologies that have been proven to work in close to industrial conditions. As a rather basic, but necessary part, we provide a technology for determining the shape of the object when passing on, e.g., a conveyor belt prior to being handled. The main technologies applicable for the manipulated objects are: A method for real-time tracking of the flexible objects during manipulation, a method for model-based offline prediction of the static deformation of grasped, flexible objects and, finally, a method for optimizing specific tasks based on both simulated and real-world executions.