Object surface reconstruction brings essential benefits to robot grasping, object recognition, and object manipulation. When measuring the surface distribution of an unknown object by tapping, the greatest challenge is to select tapping positions efficiently and accurately without prior knowledge of object region. Given a searching range, we propose an active exploration method, to efficiently and intelligently guide the tapping to learn the object surface without exhaustive and unnecessary off-surface tapping. We analyze the performance of our approach in modeling object surfaces within an exploration range larger than the object using a robot arm equipped with an end-of-arm tapping tool to execute tapping motions. Experimental results show that the approach successfully models the surface of unknown objects with a relative 59% improvement in the proportion of necessary taps among all taps compared with state-of-art performance.
翻译:对象表面的重建为机器人捕捉、 物体识别和物体操作带来重要的好处 。 在通过抓取测量未知物体的表面分布时, 最大的挑战是在不事先了解物体区域的情况下, 以高效和准确的方式选择位置。 在搜索范围下, 我们提出一种积极的探索方法, 以高效和明智的方式指导对物体表面的挖掘, 而不进行彻底和不必要的表面外挖掘 。 我们分析了在比物体更大的勘探范围内对物体表面进行建模的方法的性能 。 我们使用的是一个机器人臂, 配有武器尾部窃取工具, 用于执行抓取动作 。 实验结果显示, 这种方法成功地模拟了未知物体的表面, 与最先进的性能相比, 所有水龙中必要的水龙的比例提高了59% 。