Localization System For Autonomous Mobile Robots Using Machine Learning Methods And Omnidirectional Sonar

In the past few years, the mobile robotics has presented a growth in the research field, due to its high application area. The robot localization and its displacement in an unknown environment are fundamental to its operation. Furthemore, signal processing techniques and machine learning methods have been widely appled along with sensors into mobile robot mapping, localization and navigation. In this paper, a low cost and efficient approach for mobile robot localization using sensing by omnidirectional sonar along with signal processing techniques and machine learning methods is proposed. Regarding to classifiers, Bayes classifier, Multilayer Perceptron (MLP), k-Nearest Neighbors kNN, Support Vector Machines (SVM) and Optimum Path Forest (OPF) were analyzed. According to results, OPF using Canberra distance was the best method with accuracy of 98.13 percent and test time of 17.4us.