Embedded Object Detection and Mapping in Soft Materials Using Optical Tactile Sensing
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42979_2024_Article_2731.pdf
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2.2 MB
Format
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Checksum (MD5)
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Author(s)
Solano-Castellanos, Jose A.
Do, Won Kyung
Kennedy, Monroe D.
Date Issued
March 29, 2024
Journal
SN Computer Science
Publisher
Springer Science and Business Media LLC
Citation
SN Computer Science. 2024 Mar 29;5(4):372
Version
Final published version
Abstract
In this paper, we present a methodology that uses an optical tactile sensor for efficient tactile exploration of embedded objects within soft materials. The methodology consists of an exploration phase, where a probabilistic estimate of the location of the embedded objects is built using a Bayesian approach. The exploration phase is then followed by a mapping phase which exploits the probabilistic map to reconstruct the underlying topography of the workspace by sampling in more detail regions where there are expected to be embedded objects. To demonstrate the effectiveness of the method, we tested our approach on an experimental setup that consists of a series of quartz beads located underneath a polyethylene foam that prevents direct observation of the configuration and requires the use of tactile exploration to recover the location of the beads. We show the performance of our methodology using ten different configurations of the beads where the proposed approach is able to approximate the underlying configuration. We benchmark our results against a random sampling policy. Our empirical results show that our method outperforms the fully random policy in both the exploration and mapping phases. The exploration phase produces a better probabilistic map with fewer samples which enables an earlier transition to the mapping phase to reconstruct the underlying shape. On both the exploration and mapping phases, our proposed method presents a better consistency as compared to the random policy, with smaller standard deviation across the ten different bead configurations.
Subjects
Computer Science Applications
Computer Networks and Communications
Computer Graphics and Computer-Aided Design
Computational Theory and Mathematics
Artificial Intelligence
General Computer Science
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s42979-024-02731-6
