STABILITY PREDICTION OF QUADRUPED ROBOT MOVEMENT USING CLASSIFICATION METHODS AND PRINCIPAL COMPONENT ANALYSIS

This paper introduces a novel technique for predicting the stability of quadruped robot locomotion using a central pattern generator (CPG). The proposed method utilizes classification methods and principal component analysis (PCA) to predict stability. The objective of this study is to anticipate the stability or instability of robot movement by modifying controlling parameters, referred to as features. The simulations of robot locomotion are conducted in MATLAB/SIMULINK (R), generating a dataset of 82 observations with different parameters. Machine learn-ing (ML) techniques are then applied, using classi-fication methods and PCA, to determine the stability condition. Six classification methods, including K-nearest neighbors (KNN), support vector classifier (SVC), Gaussian Naive Bayes (GaussianNB), logistic regression (LR), decision tree (DT), and random forest (RF) are implemented using Scikit-learn, an open-source ML library in Python. The performance of these classifiers is evaluated using four metrics: precision, recall, accuracy, and F1-score. The results indicate that KNN and SVC exhibit higher metric values com-pared to the other classifiers, making them more effective for stability prediction.