Automatic Control and Guidance of Mobile Robot using Machine Learning Methods

Ghandibidgoli, S.; Mokhtari, H.

doi:10.22044/jadm.2022.11278.2286

Document Type : Methodologies

Authors

Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran.

https://doi.org/10.22044/jadm.2022.11278.2286

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. So, in current paper, 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 waist of the robot. Machine learning methods are trained using 3 large datasets read by the sensors and obtained from machine learning database of UCI. The employed methods 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 algorithm (EM), C-means, K-means, agglomerative clustering, (iv) nonparametric approaches for defining the density function: histogram and kernel estimators, (v) nonparametric 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. Computational results indicate superior performance of the implemented methods compared to the previous methods using the mentioned dataset. In general, Boosting, Bagging, Unpruned Tree and Pruned Tree (θ = 10-7) have given better results compared to the existing results. 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%.

Keywords

20.1001.1.23225211.2022.10.3.8.0

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Automatic Control and Guidance of Mobile Robot using Machine Learning Methods

References

References

Volume 10, Issue 3July 2022Pages 385-400

Volume 10, Issue 3
July 2022
Pages 385-400