Gas Source Declaration Using a Mobile Robot and a Machine Learning Algorithm in Indoor Environments

Jiyoon Lee, Purdue University

Abstract

This paper introduces a novel algorithm for gas source declaration, which is the third task of the gas source localization problem. With the algorithm, a robot should be able to determine whether the robot is located near the gas source or not. However, due to the turbulence in the gas distribution in indoor environments, lacking a strong airflow, no analytical model can be applied to the gas distribution, and it is tough to analyze the characteristics of the gas distribution. Therefore, to identify and classify the characteristics of the gas distribution, nonlinear classification algorithms are employed. Also, four feature extraction methods will be used to generate features from the gas distribution. Based on those features, the classification performance and the speed of five classifiers (MLP, KNN, SVM, AdaBoost, and Voting) were evaluated. Also, the most appropriate parameters of each classifier under the each testing environment were suggested. Using those features and parameters, the given classifiers showed the maximum success rates approximately 90.8 \% and 85.3 \% in the large and small testing rooms, respectively.

Degree

M.S.

Advisors

Matson, Purdue University.

Subject Area

Robotics

COinS