Agent skill

matlab-create-uav-scenario

Create and simulate UAV scenarios with terrain, buildings, platforms, and sensors using uavScenario. Use when building a UAV simulation, UAV simulator, or UAV scenario in MATLAB. Covers addMesh for terrain/building import, uavPlatform with updateMesh, uavSensor adaptor pattern for GPS/IMU, and the setup/advance simulation loop. Triggers on: uavScenario, UAV simulation, UAV simulator, multirotor simulation, quadrotor scenario, terrain import, building import, GPS sensor simulation.

matlabgithub.com/matlabGitHub ↗
claude-codecodexcopilotNOASSERTION
Install
npx skills add matlab/matlab-agentic-toolkit --skill matlab-create-uav-scenario --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 11 KB
Bundled scripts: none
Version: 1.0
Declared author: MathWorks
Path: skills-catalog/robotics-and-autonomous-systems/matlab-create-uav-scenario/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 868
Language: MATLAB

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Create UAV Scenario Build and simulate UAV scenarios with terrain, buildings, sensor-equipped platforms, and 3D visualization using the UAV Toolbox `uavScenario` framework. ## When to Use - Creating a UAV simulation environment with real-world terrain or buildings - Adding sensor-equipped UAV platforms (GPS, IMU, lidar) to a scenario - Running a time-stepping simulation loop with sensor readings - Visualizing UAV flight in 3D with `show3D` ## When NOT to Use - Generating flight trajectories (waypoint design, min-snap) — use trajectory skill instead - Simulink-based UAV simulation (UAV Toolbox Simulink blocks) - Path planning and obstacle avoidance algorithms - Coordinate conversion only (`ned2lla`, `lla2ned`) — use these directly ## Workflow 1. **Create scenario** — `uavScenario` with `ReferenceLocation` and `UpdateRate` 2. **Add terrain** — `addMesh(scene,"terrain",...)` with GMTED2010 or custom DTED 3. **Add buildings** — `addMesh(scene,"buildings",...)` from OSM file 4. **Add platform** — `uavPlatform` with trajectory, then `updateMesh` 5. **Attach sensors** — `uavSensor` adaptor binding sensor to platform 6. **Run simulation** — `setup` → `advance` → `updateSensors` → `read`

What's inside
Steps it walks through
  1. When to Use
  2. When NOT to Use
  3. Workflow
  4. Key Functions
  5. Patterns
  6. Create Scenario with Reference Location
  7. Add Terrain (GMTED2010)
  8. Add Buildings (OSM File)
  9. Add Platform with Mesh
  10. Attach Sensors (uavSensor Adaptor Pattern)
  11. Simulation Loop
  12. Visualize with show3D
  13. Trajectory Trail (Flight Path Line)
  14. Body-Frame Marker (Visibility in Large Scenes)
Ships with 1 file
  • manifest.yaml
More from matlab-agentic-toolkit
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About this skill
What does the matlab-create-uav-scenario skill do?

Create and simulate UAV scenarios with terrain, buildings, platforms, and sensors using uavScenario. Use when building a UAV simulation, UAV simulator, or UAV scenario in MATLAB. Covers addMesh for terrain/building import, uavPlatform with updateMesh, uavSensor adaptor pattern for GPS/IMU, and the setup/advance simulation loop. Triggers on: uavScenario, UAV simulation, UAV simulator, multirotor simulation, quadrotor scenario, terrain import, building import, GPS sensor simulation.

How do I install it?

Run `npx skills add matlab/matlab-agentic-toolkit --skill matlab-create-uav-scenario --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From matlab/matlab-agentic-toolkit, a repository with 868 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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