Robot-aided Microbial Density Estimation and Mapping
J. J. J. Pey, A. P. Povendhan, T. Pathmakumar, M. R. Elara
Abstract
Estimating the microbial infestation profile of an area is essential for an effective cleaning process. However, current methods used to inspect the microbial infestation within a spatial region are manual and laborious. For large regions that require automated cleaning, conventional methods of microbial examination are not practical. We propose a novel robot-aided microbial density estimation and mapping framework using an in-house developed biosensor payload onboard a mobile robot. The biosensor estimates the degree of microbial infestation in Relative Light Units (RLU) using the natural bio-luminescence reaction. The global distribution of microbial infestation is approximated through the Radial Basis Function (RBF) and Nearest Neighbour (NN) interpolation algorithms. The proposed method is implemented on an in-house developed mobile robot called Beluga. The framework's validation and usefulness are demonstrated quantitatively through real-world experiment trials.
BibTeX
@inproceedings{iros2022_robotaidedmicrob,
title = {Robot-aided Microbial Density Estimation and Mapping},
author = {J. J. J. Pey and A. P. Povendhan and T. Pathmakumar and M. R. Elara},
booktitle = {IROS 2022},
year = {2022}
}