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.
Bibliographical notePublisher Copyright:
© 1996-2012 IEEE.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering