In this project, machine learning based techniques for real time terrain identification of the autonomous robots are investigated. The factors affecting the performance of autonomous robots include nature of trajectories, on-course obstacles, and nature of terrain. The challenges involved in understanding the terrain of autonomous robots are called localization problems. This project investigates a robust classification based machine learning model to identify the terrains of an autonomous robot from a set of input sensor data , which would incorporated as features in the model. The features are selected with respect to the kinematic and dynamic model of differential drive robots. The terrains are classified into 11 classes and the inputs from different sensors are measured and categorized into the respective classes. A total of 49345 readings were taken. Twenty three classification learning methods are evaluated to find the best fitting model that can identify the terrains of robots in real time. Ensemble Subspace KNN classification learning model produced an accuracy of 100 %, observed as the best model for terrain identification. The results are represented using confusion matrix, which shows the relation between original terrains and model predicted terrains , scatter plot that represents the relationship between each features and ROC Curve analyses each sensor input data. The model output can be provided to an intelligent mechanism to control the wheels of robots and improve their performance.
