Mecanum-Wheeled Autonomous Mobile Robot for Flexible Manufacturing System

Autonomous Mobile Robots (AMRs) are increasingly utilized across various industrial sectors, particularly in manufacturing. This study focuses on the design and implementation of an AMR equipped with four Mecanum wheels, programmed to execute diverse path planning tasks within a simulated manufacturing environment. The primary objectives are to determine the AMR's position, plan its movements, and navigate around obstacles. To achieve these goals, the AMR employs a Simultaneous Localization and Mapping (SLAM) algorithm based on the Robot Operating System (ROS), installed on a Raspberry Pi. A Light Detection and Ranging (LIDAR) sensor is used for mapping. The path planning algorithm integrates the Dijkstra algorithm for global planning and the Dynamic Window Approach (DWA) for local planning, allowing the AMR to navigate autonomously based on a predefined map. User-defined target movements are executed via optimal paths determined by the AMR's navigation plan. The proposed algorithm and the AMR's navigation system were tested both in real-world path planning scenarios and in simulated environments using RViz, demonstrating their effectiveness and reliability.