Automatic Control and Guidance of Mobile Robot using Machine Learning Methods

Abstract

In many applications of the robotics, the mobile robot should be guided from a source to a specific destination. The automatic control and guidance of a mobile robot is a challenge in the context of robotics. Thus, in the current work, this problem is studied using various machine learning methods. Controlling a mobile robot is to help it to make the right decision about changing direction according to the information read by the sensors mounted around the waist of the robot. The machine learning methods are trained using 3 large datasets read by the sensors and obtained from the machine learning database of UCI. The methods employed include (i) discriminators: greedy hypercube classifier and support vector machines, (ii) parametric approaches: Naive Bayes’ classifier with and without dimensionality reduction methods, (iii) semiparametric algorithms: expectation-maximization (EM) algorithm, C-means, K-means, agglomerative clustering, (iv) non-parametric approaches for defining the density function: histogram and kernel estimators, (v) non-parametric approaches for learning: k-nearest neighbors and decision tree, and (vi) combining multiple learners: boosting and bagging. These methods are compared based on various metrics. The computational results indicate the superior performance of the implemented methods compared to the previous ones using the mentioned dataset. In general, boosting, bagging, unpruned tree, and pruned tree (θ = 10-7) have given better results compared to the existing ones. Also, the efficiency of the implemented decision tree is better than the other employed methods, and this method improves the classification precision, TP-rate, FP-rate, and MSE of the classes by 0.1%, 0.1%, 0.001%, and 0.001%.