This article presents a novel multifidelity-based optimal sampling method to rapidly estimate the shape of an object from the touch-down points on its surface given a highly limited number of sampling trials. The proposed approach attempts to improve the existing shape estimation via tactile exploration, which uses Gaussian process regression for implicit surface modeling with sequential sampling. The main objective is to make the process of sample point selection more efficient and systematic such that the unknown shape can be estimated fast and accurately with highly limited sample points (e.g., less than 1% of the original dataset). Specifically, we propose to select the next best sample point based on two optimization criteria: 1) the mutual information (MI) for uncertainty reduction, and 2) the local curvature for fidelity enhancement. The combination of these two objectives leads to an optimal sampling process that balances between the exploration of the whole shape and the exploitation of the local area where the higher fidelity (or more sampling) is required. Simulation and experimental results successfully demonstrate the advantage of the proposed method in terms of estimation speed and accuracy over the conventional methods. Our approach allows us to reconstruct recognizable three dimensional shapes using only around optimally selected 0.4% of the original dataset.
Bibliographical noteFunding Information:
Manuscript received May 21, 2019; revised September 19, 2019; accepted September 27, 2019. Date of publication October 22, 2019; date of current version March 17, 2020. This work was supported in part by the National Sciences and Engineering Research Council of Canada under Grant RGPIN-2015-05273, Grant RTI-2018-00414, Grant STPGP 506987, and Grant Canada Foundation for Innovation LOF 30788, in part by the Mid-Career Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under Grant NRF-2018R1A2B6008063, and in part by the National Research Foundation of Korea funded by the Korea government (MSIT) under 2018R1A4A1025986. Paper no. TII-19-1973. (Corresponding author: Jongeun Choi.) S. Yang and S. Jeon are with the Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada (e-mail: email@example.com; soojeon@ uwaterloo.ca).
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering