基于高斯过程的有效未知物体表面重构的主动抽头算法
Active Tapping via Gaussian Process for Efficient Unknown Object Surface Reconstruction
摘要：
物体表面重构对于机器人抓取、物体识别和物体操作具有重要意义。在利用敲击法测量未知物体表面分布时，最大的挑战是在没有物体区域先验知识的情况下，高效、准确地选择敲击位置。在给定搜索范围的情况下，提出了一种主动探索方法，有效地、智能地引导敲击学习物体表面，而不需要穷举和不必要的离表面敲击。我们分析了我们的方法在建模对象表面的探索范围内大于使用机器人手臂配备了一个末端的臂攻丝工具来执行攻丝运动的对象的性能。实验结果表明，该方法成功地模拟了未知物体的表面，与最先进的性能相比，在所有抽头中的必要抽头的比例相对提高了59%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
