Designing Robotic Desktop Assistants Using Dynamics-based Shape-Sensing

IIS-9820180Erdmann， MichaelCarnegie Mellon大学$63,876 - 12 mos。使用基于动态的形状感应设计机器人桌面助手这是一个为期三年的持续奖的第一年资助。本研究旨在了解形状感知机器人操作，以提高机器人与环境交互的多功能性。随着机器人对人类的辅助能力越来越强，它们将需要操纵各种各样形状和大小各异的新物体。识别和操纵这些物体的关键将是许多被动和主动感官模式的整合。本研究的重点是主动触觉操作策略的信息含量。例如，当抓取形状和动力学已知但姿态未知的物体时，手指接触点的时间历史通常提供足够的约束来揭示物体的方向。更困难的任务是捡起一个形状和动力学未知的物体。同样，触摸物体所需的手指配置为物体的可能形状提供了限制。随着手指的移动，物体在手指中滑动，这些限制随着时间的推移而演变，从而允许重建物体的形状。这些任务之间的区别主要在于触摸物体所需的手指数量，主动操作的程度以及内部模型的特征。在这些已知和未知形状的极端之间存在着更多的典型任务，其中存在部分形状信息。例如，人类或机器人可以通过视觉检查知道物体的大致形状和姿势；抓取策略揭示了精细的形状和动态。这项研究最直接的影响在于操作，可能对触觉显示和工业自动化产生额外的影响。这项研究产生的形状感应程序将使机器人编程变得更加容易。这项研究将提供在机器人操作过程中提取信息的自动方法，而不是要求机器人程序员精确地测量物体的形状和位置。这种自动化方法将促进机器人辅助。此外，了解在操作过程中产生的形状信息的性质将有助于设计更好的触觉设备。

Abstract IIS-9820180Erdmann, MichaelCarnegie Mellon University $63,876 - 12 mos.Designing Robotic Desktop Assistants Using Dynamics-Based Shape-SensingThis is the first year funding of a three year continuing award. This research seeks to understand shape-aware robotic manipulation in order to improve the versatility of robots interacting with their environments. As robots become more human-assistive, they will need to manipulate a large variety of novel objects of varying shapes and sizes. Key to recognizing and manipulating these objects will be the integration of numerous passive and active sensory modalities. This research focuses on the information content of active tactile manipulation strategies. For instance, when grasping an object of known shape and dynamics but unknown pose, the time history of the contact points on the fingers often provides enough constraint to reveal the orientation of the object. More difficult is the task of picking up an object of unknown shape and unknown dynamics. Again, the finger configurations required to touch the object provide constraints on the possible shapes of the object. As the fingers move and the object slips in the fingers, these constraints evolve over time, thereby permitting reconstruction of the shape of the object.The difference between theses tasks lies primarily in the number of fingers required to touch the object, the degree of active manipulation, and the character of internal models. In between these extremes of known and unknown shape lie more typical tasks, in whichpartial shape information exists. For instance, a human or robot may know the rough shape and pose of an object from visual inspection; grasping strategies then reveal the fine shape and dynamics. The impact of this research lies most directly in manipulation, with possible additional impact on haptic displays and industrial automation. The shape-sensing routines produced by this research will make it easier to program robots. Rather than requiring a robot programmer to measure object shapes and locations precisely, thisresearch will provide automatic methods for extracting that information during robot manipulation. Such automatic methods will facilitate robotic aids. Moreover, understanding the nature of the shape information produced during manipulation will facilitate the design of better haptic devices.