The project faced several challenges, beginning with hardware constraints, such as the physical limitations of wheel traction and battery lifespan, which affected motion stability and operational time. The integration of various ROS packages, some with incomplete documentation and inconsistent coding practices, complicated the development of a reliable and maintainable codebase. The method adopted involved precise sensor calibration to ensure accurate perception and control, incorporating camera intrinsic and extrinsic calibration for improved visual data interpretation, and adjusting wheel parameters to maintain balanced motion. The lane following module required parameter tuning for gain, trim, and heading correction to adapt to Duckietown’s environment. The original FSM-based intersection navigation system was re-engineered due to unreliability in node transitions, replaced with a distance-based approach for intersection stops and turns, ensuring deterministic and reliable behavior. Dijkstra’s algorithm was implemented to create a structured graph representation of the city map, enabling dynamic path planning that adapts to real-time inputs from the perception system. Custom web dashboards built with React.js and roslibjs facilitated monitoring and debugging by providing live data feedback and control interfaces. Through this rigorous and iterative process, the project achieved a robust autonomous navigation system capable of precise path planning and safe maneuvering within Duckietown.