Design and Implementation of an Autonomous Mobile Robot for Intralogistics: A Case Study with Siemens Technology and Interoperability

This thesis develops and validates an Autonomous Mobile Robot solution for intralogistics, using a real industrial case study at IMA Automation. Its objective is to connect research-level autonomy with the safety, determinism, and interoperability required on the shop floor. The AMR is engineered using Siemens’ SIMOVE stack, comprising Carrier Control, ANS+ laser localization, and Master Control, implemented in TIA Portal and validated through a digital twin for virtual commissioning. A discrete-event material-flow model, calibrated to IMA’s process of rack transfers between the AutoStore interface and the consolidation bays, is used to assess fleet sizing, mission allocation policies, and charging strategies under realistic demand profiles. Prototype experiments measure positioning accuracy, braking distances, and energy consumption as functions of payload, acceleration, and state of charge, providing data-driven safety margins and return-to-charge thresholds. In parallel, a ROS 2/Navigation2 ‘research bridge’ is developed to enable rapid experimentation in mapping, global planning, and obstacle avoidance, while preserving the deterministic safety framework provided by SIMOVE. To support future scalability and multivendor coordination, the work also analyzes the VDA 5050 interface and outlines a roadmap for its integration with the fleet manager and higher-level enterprise systems. Results confirm the solution’s feasibility and yield actionable guidelines: two active AMRs plus one reserve satisfy throughput requirements, while maintaining availability across shifts; and an adaptive charging strategy preserves performance with minimal idle time. The thesis delivers an end-to-end methodology, encompassing mechatronics, digital twins, flow simulation, and interoperability, that reduces commissioning risk and accelerates the deployment. A roadmap for VDA 5050–based fleet coordination and hybrid ROS-assisted autonomy in production environments conclude the work.