What problem does it solve? Grid-based planners like A* and NavFn produce staircase-like paths constrained to grid edges, causing unnecessary turns and controller oscillation. This Skill provides the configuration and tuning knowledge to set up the Nav2 Theta* planner, which uses line-of-sight checks to generate straighter, any-angle paths. ## Core Features & Use Cases - Theta Plugin Configuration*: Complete YAML setup for the nav2_theta_star_planner::ThetaStarPlanner plugin within the Nav2 planner_server. - Parameter Tuning Guidance: Explains how_many_corners, w_euc_cost, w_traversal_cost, terminal_checking_interval, and allow_unknown, including the cost function trade-offs between path shortness and obstacle avoidance. - Planner Comparison & Troubleshooting: Compares Theta* against NavFn and SMAC 2D, and diagnoses issues like grid-aligned paths, slow planning, and paths cutting too close to obstacles. - Use Case: A robotics engineer tuning a differential-drive indoor robot wants smoother diagonal paths without the computational cost of Hybrid-A*, and uses this Skill to configure Theta* with appropriate cost weights. ## Quick Start Configure the Nav2 planner_server to use the Theta* planner plugin with 8-connected neighbors and balanced cost weights for my indoor mobile robot.