This article proposes a novel efficient sampling strategy to rapidly estimate the distribution of stiffness over an inhomogeneous object with a highly limited number of sample points taken from the object surface. The stiffness on the object surface is modeled as a mass–spring system, and its distribution is estimated via tactile exploration using Gaussian process regression. The main objective of this article is to improve the efficiency of the estimation process while producing an accurate estimate for both the overall distribution and some particular areas (i.e., high/low stiff areas). Specifically, the mutual information is employed to quantify the amount of information on the whole space of interest provided by each sample point. The estimated stiffness distribution is also taken into account to locate the extreme stiffness areas. An objective function that consists of these two criteria is proposed to optimally balance between the exploration of the unobserved regions and exploitation of certain local areas that have high/low stiffness. Physical experiments on a variety of inhomogeneous objects demonstrate the advantage of the proposed algorithm in comparison to a popular existing algorithm in terms of accuracy and estimation speed.

中文翻译：

---

通过主动触觉探索快速估计3-D刚度分布的有效采样

本文提出了一种新颖的有效采样策略，可以快速估计非均匀物体上刚度的分布，并且从物体表面获取的采样点数量非常有限。将物体表面的刚度建模为质量-弹簧系统，并通过使用高斯过程回归的触觉探索来估计其分布。本文的主要目的是提高估算过程的效率，同时针对整体分布和某些特定区域（即高/低刚性区域）产生准确的估算。具体地，互信息用于量化由每个采样点提供的有关整个感兴趣空间的信息量。定位刚度分布时也要考虑估算的刚度分布。提出了由这两个标准组成的目标函数，以在未观察区域的探查与具有高/低刚度的某些局部区域的探查之间达到最佳平衡。在各种不均匀物体上的物理实验证明，与现有的流行算法相比，该算法在准确性和估计速度方面具有优势。