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name: navigation-and-planning description: "This skill guides an agent through IR-SIM reactive navigation, multi-agent avoidance, optional ORCA coordination, and programmatic path planning." disable-model-invocation: true metadata: disco-role: operating license: MIT

Navigation and planning

Use this route when an agent must select or configure a goal-seeking behavior, avoid other agents or line obstacles, coordinate a group, or plan a path on an IR-SIM map. It covers the built-in dash, rvo (including vo and hrvo modes), and sfm behaviors, the optional orca group behavior, and the six public planners.

Route quickly

Need	Read next
Individual behavior names, kinematics compatibility, parameters, and line obstacles	behaviors.md
get_map, planner constructors, planning, output shape, and failure recovery	path-planners.md
group_behavior: {name: orca} and the pyrvo gate	optional-orca.md
Install/import, invalid configuration, blocked maps, or headless failures	troubleshooting.md

Run the bundled bounded checks from any working directory:

python skills/disco/ir-sim/sub-skills/navigation-and-planning/scripts/behavior_smoke.py --help
python skills/disco/ir-sim/sub-skills/navigation-and-planning/scripts/planner_smoke.py --help

The helpers create temporary YAML scenes, disable rendering, use a small step or iteration budget, and never depend on the source checkout's example files. They are smoke checks, not proof that a large crowd or optional ORCA run is correct. ORCA is never reported as verified by the helpers unless pyrvo is independently importable; see optional-orca.md.

Minimal workflow

1. Install the base package (pip install ir-sim) and import the planner or behavior surface. Install ir-sim[all] only when ORCA, keyboard input, or video output is actually needed.
2. Author a compatible robot/obstacle scene. Kinematics and geometry belong to scene-configuration; sensor-derived maps belong to sensing-and-mapping.
3. For reactive navigation, give each robot a goal and a compatible behavior. Use group_behavior only for coordinated group control. Step a headless environment with display=False, save_ani=False, and a fixed seed while tuning.
4. For global planning, call env.get_map(resolution=...), construct the planner with the map (and the robot or radius for sampling planners), call planning(start, goal, show_animation=False), and draw only a non-empty result with env.draw_trajectory(path, traj_type="r-"). A planner path is a 2 x N array in goal-to-start order in this release; reverse it before a follower that expects start-to-goal order.
5. Keep planning, following, stepping, and rendering separate. Lifecycle and headless cleanup are covered by simulation-environments; custom registries and external controllers belong to extension-and-control.

Selection rule

• Choose dash for direct, non-avoiding motion and for a simple path follower.
• Choose rvo for geometric local avoidance. Set mode: vo for one-sided velocity obstacles, mode: hrvo for the hybrid variant, and leave mode: rvo for reciprocal behavior. These are modes of the rvo behavior, not additional YAML behavior names.
• Choose sfm when anisotropic, pedestrian-like interaction and wall forces matter more than a hard collision-free guarantee. Tune it with a bounded test scene; it is not a global planner.
• Choose orca only after the optional dependency probe succeeds. It is a group behavior, not an individual behavior value.
• Choose A*/JPS for grid search, RRT for a quickly found feasible sample path, RRT* for rewiring-based improvement, Informed RRT* for informed improvement after an initial solution, and PRM for repeated queries on a static map.

For parameter trade-offs, exact live signatures, and known return-value quirks, use the linked references rather than guessing from a behavior name.