Payload transportation system of a learning factory

Abstract

The transition to Industry 4.0 has highlighted the urgent need for a skilled workforce proficient in operating and maintaining advanced robotic and automation systems. To address this critical issue and prepare the next generation of industry professionals, this thesis focuses on the design and implementation of a payload transportation system for a Learning Factory. The proposed system combines SLAM, AR Tracking, and ROS Navigation technologies to enable efficient navigation and obstacle avoidance within the Learning Factory environment. Additionally, the system facilitates seamless cooperation with a manipulator robot, enabling collaborative tasks and enhancing the overall efficiency of the system. The outcome of this work is demonstrated in a book transportation scenario incorporating a multi-robot system that consists of two manipulator robots, and a mobile base. This work contributes to bridging the skills gap and equipping the next generation of industry professionals with practical robotics knowledge.