Optimizing MiR100 Performance in Internal Logistics through Digital Twin-Based Dynamic Speed Adjustment

Internal logistics is a part of the logistics that deals with the flow of materials or information within the organization. In the manufacturing companies, this is focused on the delivery of materials or semi-finished products within the manufacturing companies. Thanks to technological advancement various industries' operation particularly internal logistics has significantly changed. This evolution is driven by Industry 4.0, under which manufacturing industries have embraced automation and smart manufacturing to digitalize these internal flows by using autonomous mobile robots (AMR) or automated guided vehicles (AGV). While automating logistics process eliminates many manual and repetitive operations, it also introduces new challenges such as use the vehicles as effectively as possible and minimizing their energy consumptions as they are dependent on energy sources such as batteries. Therefore, to achieve operational and sustainability objectives in manufacturing framework at the same time, an industry 5.0 approach, which focuses on protecting the environment and achieving resilient industrial operations, is introduced. Speed of AMRs and AGVs is one of the critical factors in energy consumption, higher speeds deplete the battery much faster. By integration of digital twin technology with AMRs, manufacturers can have an opportunity to monitor real-time data and collateral adjustment based on a continuous performance evaluation, facilitating quick responses to changing production conditions to achieve maximum transport efficiency and improving manufacturing performance. The focus of study is on optimizing energy consumption through speed adjustment while simultaneously improving operational efficiency. This thesis aims to contribute to leveraging digital twin technology with Autonomous mobile robot, specifically MiR100 within manufacturing frameworks. The research framework is implemented in Mind4Lab at Politecnico di Torino, to collect real time data of the MiR100 robot utilizing Node-Red, FlexSim simulation software and the Modbus communication protocol. The first part of the research is aimed at evaluating energy consumption and battery behavior across different speeds, specifically high-speed and low-speed scenarios, while the second part of the study contributes to leveraging digital twin framework that allows dynamic speed regulation in response to real-time data demonstrating how this can enhance energy and operational efficiency.