[Nav2][Discussion] Systematic approach for tuning Nav2 planners and controllers?

A developer in the ROS (Robot Operating System) Nav2 community has sparked a significant discussion by highlighting a critical pain point in robotics development: the manual, iterative, and inefficient process of tuning navigation parameters. The user, working with an Ackerman-drive vehicle, described a tedious cycle of adjusting parameters in YAML configuration files, running benchmark navigation goals in the Gazebo simulator, and visually observing behavior in tools like Foxglove. This approach is particularly challenging for complex navigation stacks using the SMAC Hybrid A* planner and the Model Predictive Path Integral (MPPI) controller, which contain a large number of interdependent parameters. The goal is to find a single configuration that performs well across diverse benchmarks, including full environment loops, U-turns, and tight corner maneuvers.

The post has catalyzed a search for systematic solutions, moving beyond ad-hoc tuning. The developer pointed to existing community resources like a Nav2 tuning guide and a prior discussion on quantitative evaluation metrics but emphasized the need for more robust methodologies. A key suggestion raised is borrowing techniques from machine learning, specifically automated hyperparameter optimization frameworks like Optuna. This approach could systematically search the parameter space to optimize for defined performance metrics. The core question extends beyond automation to fundamental evaluation: how should navigation performance be quantitatively measured—using metrics like path smoothness, completion time, or deviation from an ideal path—rather than relying solely on subjective visual inspection? This discussion is vital for advancing the Nav2 framework's usability and enabling more reliable autonomous navigation in real-world robotics applications.