What problem does it solve? Choosing the wrong Nav2 global planner leads to jagged paths, controller oscillation, or kinematically infeasible trajectories, and teams often blame the planner when the real fault lies in the costmap, controller, or transforms. ## Core Features & Use Cases - Decision Matrix: Compares NavFn, SMAC 2D, SMAC Hybrid-A*, SMAC Lattice, and Theta* across algorithm, search space, speed, smoothness, kinematic feasibility, and memory. - Robot-Type Recommendations: Maps differential drive, Ackermann steering, and holonomic robots to suitable planners with concrete parameter guidance such as minimum_turning_radius and motion_model_for_search. - Misdiagnosis Checklist: Distinguishes planner problems from costmap inflation issues, controller tuning faults, and TF timing errors before switching planners. - Use Case: An Ackermann robot like an RC-scale car cannot follow NavFn's grid-aligned paths, so you configure SMAC Hybrid-A* with Reeds-Shepp motion primitives and select it at runtime via the planner_id field. ## Quick Start Ask which Nav2 planner fits an Ackermann-steering robot and how to configure it in the planner server.
