MCFS-UC: A Novel Mobile Robot Navigation Feature Selection Method for Optimal Sensor Readings in IIoT Environments

Nowadays, mobile robot navigation is a crucial topic in the Industrial Internet of Things, and a number of sensors are arranged around robots to avoid obstacles on navigation paths. Obtaining optimal sensor readings that can be used to optimize the path planning of mobile robots is essential. Thus, an effective feature selection technique is explored in this study to select the optimal subset of sensor readings for mobile robot navigation. Feature selection can reasonably remove irrelevant and redundant features, reduce the dimensionality of data, and improve the learning accuracy and comprehensibility. A novel feature selection method based on 3D mutual information is studied. First, the proposed method defines symmetrical uncertainty with 3D mutual information to measure the correlation between candidate features and select features. Afterward, a merit function of feature sets is defined based on the symmetrical uncertainty to search for the optimal feature subset. Lastly, the proposed feature selection method is applied to a wall-following robot navigation data set. Results show that the proposed method can obtain few sensor readings but with enhanced prediction accuracy of the robot's movements.