Costmap Tuning for Autonomous Navigation: A Simulation and Real-World Study on the Hiwonder JetAcker

The pursuit of reliable mobile robot autonomy continues to hinge on the nuanced configuration of its navigation subsystems. Within the widely adopted Robot Operating System (ROS), the layered costmap serves as the critical environmental representation, yet its numerous parameters are notoriously difficult to tune by hand. This empirical struggle often leads to deployments that are either overly cautious or dangerously optimistic. Our investigation focuses on demystifying this process through a structured analysis of two pivotal parameters, the inflation radius and the robot radius. We conducted a series of simulated navigation trials using the Hiwonder JetAcker platform in ROS Noetic, meticulously measuring outcomes related to path success, efficiency, and adherence to safety margins. These simulation findings were then cautiously validated through a set of targeted hardware experiments. Our results demonstrate that empirically best-performing inflation radius of 0.3 m for the Hiwonder JetAcker platform reduces failure rates in constrained spaces by 40% compared to the conventional default of 0.6 m. Furthermore, our work quantifies the gap between simulated and real-world navigation. The resulting framework offers practitioners a more informed methodology for systematic parameter tuning and underscores the non-trivial consequences of seemingly minor configuration changes.