ROS2-Based Motion Control Learning Framework for Differential Drive Mobile Robots
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Abstract
This study aims to design and develop a motion control teaching aid for a differential wheel drive mobile robot as a learning medium in robotic courses. The system is implemented using the NVIDIA Jetbot AI platform integrated with the Robot Operating System (ROS2). The research methodology consists of planning, design, and implementation stages, covering wheel control and a preliminary autonomous control scheme. During the design stage, a differential drive control block diagram was developed by integrating trajectory reference input, robot kinematics, motor actuation, and wheel encoder feedback. The system implementation involves the development of multiple ROS2 nodes that communicate through standard topics, along with a web-based interface that supports both manual and autonomous control modes. Initial testing results indicate that the wheel control system provides stable motion response, while the ROS2-based architecture and web interface facilitate real-time monitoring and enhance students’ understanding of motion control concepts. This research is expected to support effective learning of motion control and autonomous mobile robot navigation in higher education.
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