Classification Techniques for Wall-Following Robot Navigation: A Comparative Study

Autonomous navigation is an important feature that allows the robot to move independently from a point to another without a teleoperator. In this paper, an investigation related to mobile robot navigation is presented. A group of supervised classification algorithms are tested and validated using the same dataset. Then focus will shift especially towards the k-Nearest Neighbors (KNN) algorithm. In order to improve the performance of KNN, an existing work related to genetic algorithms, local search, and Condensed Nearest Neighbors termed Memetic Controlled Local Search algorithm (MCLS) is applied to overcome the high running time of KNN. The results indicate that KNN is a competing algorithm especially after decreasing the running time significantly and combining that with existing algorithm features.