  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.
