Autonomous mobile robots for recycling metal shaving at CNC factories

The aim of this study is to develop an autonomous mobile robot (AMR) for our demonstration factory, incorporating Google's Cartographer algorithm with the linear quadratic Gaussian (LQG) control model and providing safe navigation with obstacle avoidance for the delivery of recycling metal shaving (RMS) with the help of this Cartographer-LQG method. The originality of this study is that we have integrated Google's Cartographer algorithm with the LQG model and improved the accuracy and stability of our AMR-RMS. This method offers users a reliable method for calibrating their mobile robots and constructs a grid map with loop-closure to automate navigation. The suggested approach increases the stability of the electro-mechanical modules and lowers the cumulative error of simultaneous localization and mapping (SLAM). This study has compared the SLAM results from Gmapping, Hector, and Cartographer algorithms, suggesting that the Cartographer-LQG method can provide a map with loop closure and accurate information for autopiloting the AMR-RMS.