Digital Twin-Based Decision-Making Technique for Diagnostic 2D Environment Line Following Error of Mobile Robot

Significant progress in mobile robotics has recently made robots more capable of navigating complex environments independently. However, mobile robots may need help accurately tracking lines in two-dimensional (2D) settings due to several factors, including sensor noise, environmental uncertainties, and mechanical flaws. To address this issue, this study presented a new approach that utilizes Digital Twin (DT) technology to assist decision-making processes in identifying and reducing line-following errors in mobile robots while navigating within two-dimensional settings. The proposed technique involves creating a virtual representation of a DT mobile robot. This virtual counterpart is updated in real time to copy the actions of the physical robot, using color sensor data, fuzzy control algorithms, and relevant parameters. This simulation analyzes the robot's performance and identifies causes of line-following errors. The DT -based Decision-Making Technique uses fuzzy logic to evaluate underlying factors contributing to errors. Extensive simulations and real-world experiments were conducted on a mobile robot, specifically the Lego Mindstorms NXT, to validate the proposed approach's effectiveness. The robot was tasked with navigating 2D environments during these experiments. The study results show that the DT -based Decision-Making Technique is a valuable tool for enhancing the adaptability and dependability of autonomous robots in complex, two-dimensional settings. By identifying and correcting line-following errors, this technique improves the performance of robots and has practical applications in various fields, including warehouse automation, surveillance, and exploration missions.