A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot

TL;DR

This paper compares various machine learning algorithms for controlling a wall-following robot, achieving high accuracy and providing insights into their performance on sensor fusion tasks.

Contribution

It introduces new high-accuracy models for different sensor input formats and offers a comprehensive comparison of machine learning and deep learning algorithms.

Findings

Decision Tree Classifier achieved 100% accuracy with 4 and 2 sensor inputs.

Gradient Boosting Classifier achieved 99.82% accuracy with 24 sensors.

The study provides insights into algorithm performance for sensor fusion problems.

Abstract

A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper. The models were trained using an open-source dataset that contains 24 ultrasound sensors readings and the corresponding direction for each sample. This dataset was captured using SCITOS G5 mobile robot by placing the sensors on the robot waist. In addition to the full format with 24 sensors per record, the dataset has two simplified formats with 4 and 2 input sensor readings per record. Several control models were proposed previously for this dataset using all three dataset formats. In this paper, two primary research contributions are presented. First, presenting machine learning models with accuracies higher than all previously proposed models for this dataset using all three formats. A perfect solution for the 4 and 2 inputs sensors formats is presented using Decision Tree Classifier by achieving a mean accuracy of 100%. On the other hand, a mean accuracy of 99.82% was achieves using the 24 sensor inputs by employing the Gradient Boost Classifier. Second, presenting a comparative study on the performance of different machine learning and deep learning algorithms on this dataset. Therefore, providing an overall insight on the performance of these algorithms for similar sensor fusion problems. All the models in this paper were evaluated using Monte-Carlo cross-validation.