Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier

The navigation of mobile robots throughout the surrounding environment without collisions is one of the mandatory behaviors in the field of mobile robotics. The movement of the robot through its surrounding environment is achieved using sensors and a control system. The application of artificial intelligence could potentially predict the possible movement of a mobile robot if a robot encounters potential obstacles. The data used in this paper is obtained from a wall-following robot that navigates through the room following the wall in a clockwise direction with the use of 24 ultrasound sensors. The idea of this paper is to apply genetic programming symbolic classifier (GPSC) with random hyperparameter search and 5-fold cross-validation to investigate if these methods could classify the movement in the correct category (move forward, slight right turn, sharp right turn, and slight left turn) with high accuracy. Since the original dataset is imbalanced, oversampling methods (ADASYN, SMOTE, and BorderlineSMOTE) were applied to achieve the balance between class sam-ples. These over-sampled dataset variations were used to train the GPSC algorithm with a random hyperparameter search and 5-fold cross-validation. The mean and standard deviation of accuracy (ACC), the area under the receiver operating characteristic (AUC), precision, recall, and F1 - score values were used to measure the classification performance of the obtained symbolic expressions. The investigation showed that the best symbolic expressions were obtained on a dataset balanced with the BorderlineSMOTE method with ACC +/- SD(ACC), AUC(macro) +/- SD(AUC), Precision(macro) +/- SD(Precision), Recall(macro) +/- SD(Recall), and F1 - score(macro) +/- SD(F1 - score) equal to 0.975 x 1.81 x 10(-3), 0.997 +/- 6.37 x 10(-4), 0.975 +/- 1.82 x 10(-3), 0.976 +/- 1.59 x 10(-3), and 0.9785 +/- 1.74 x 10(-3), respectively. The final test was to use the set of best symbolic expressions and apply them to the original dataset. In this case the ACC +/- SD(ACC), AUC +/- SD(AUC), Precision +/- SD(Precision), Recall +/- SD(Recall), and F1 - score +/- SD(F1 - Score) are equal to 0.956 +/- 0.05, 0.9536 +/- 0.057, 0.9507 +/- 0.0275, 0.9809 +/- 0.01, 0.9698 +/- 0.00725, respectively. The results of the investigation showed that this simple, non-linearly separable classification task could be solved using the GPSC algorithm with high accuracy.