What problem does it solve?
Mobile robot navigation in Isaac Sim requires correctly deriving robot footprints, generating occupancy maps from USD stages, planning collision-free paths, and sizing navigation buffers—tasks that are error-prone when hardcoded or done without physics-aware validation.
Core Features & Use Cases
- Runtime Footprint Derivation: Computes robot footprint dimensions, Z-offset, and inscribed/circumscribed radii from collider prims, preventing robots from falling through floors or clipping walls.
- Occupancy Mapping & A Planning*: Rasterizes USD stage geometry into occupancy grids with obstacle filtering, then runs A* with erosion kernels and oriented-footprint PhysX validation.
- Wheel Kinematics & Cameras: Provides differential drive and holonomic controller configurations plus look-at chase camera math with degenerate up-vector handling.
- Use Case: When building a warehouse navigation demo with a Nova Carter robot, use this Skill to compute its footprint, generate an occupancy map from the warehouse USD, plan an A* route through aisles, and validate every waypoint against PhysX overlap queries before driving.
Quick Start
Ask the AI to compute the robot footprint and plan a validated A* path across an occupancy map for a mobile robot in your Isaac Sim stage.