Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier

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@Article{Andelic:2023:Machines,

• author = "Nikola Andelic and Ivan Lorencin and Sandi {Baressi Segota} and Zlatan Car",
• title = "Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier",
• journal = "Machines",
• year = "2023",
• volume = "11",
• number = "1",
• pages = "Article no 105",
• month = jan,
• email = "nandelic@riteh.hr",
• keywords = "genetic algorithms, genetic programming, classification of robot movement, oversampling methods, symbolic classifier, ultrasound sensors",
• publisher = "MDPI",
• ISSN = "2075-1702",
• URL = "https://www.mdpi.com/2075-1702/11/1/105",
• DOI = "doi:10.3390/machines11010105",
• size = "35 pages",
• abstract = "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 samples. 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. 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.",

}

Genetic Programming entries for Nikola Andelic Ivan Lorencin Sandi Baressi Segota Zlatan Car

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