Building a ROS 2 Robot Map Viewer with Kotlin

#android #kotlin #robotics #ros2

Building a ROS 2 Robot Map Viewer with Kotlin

What You Will Build

In this tutorial, you will build a practical Kotlin/Android component for a robotics or Physical AI system. The design emphasizes asynchronous processing, lifecycle-aware state, real-time data handling, observability, and safe separation between the Android interface and physical robot control.

Architecture

Android Kotlin + Jetpack Compose
             ↓
       ViewModel / Flow
             ↓
      Repository / API
             ↓
   ROS 2 / Jetson / AI Backend
             ↓
        Robot System

Step 1 — Define map data

data class MapPoint(
    val x: Float,
    val y: Float
)

Step 2 — Receive map updates

val mapFlow: Flow<List<MapPoint>> = rosBridge.mapFlow()

Step 3 — Collect lifecycle-safely

val points by mapFlow.collectAsStateWithLifecycle(emptyList())

Step 4 — Render the map

Canvas(Modifier.fillMaxSize()) {
    points.forEach {
        drawCircle(
            radius = 2f,
            center = Offset(it.x, it.y)
        )
    }
}

Step 5 — Optimize rendering

Do not redraw the entire map for every unrelated UI state change. Sample high-frequency updates and keep coordinate transformations outside expensive composables where possible.

Performance Checklist

Keep CPU-heavy work off the main thread.
Use bounded buffers for high-rate streams.
Prefer StateFlow for observable UI state.
Sample high-frequency telemetry before rendering.
Measure end-to-end latency instead of only model latency.
Handle reconnects and stale data explicitly.
Keep emergency controls independent of high-bandwidth streams.

Testing Checklist

Test with no network connection.
Test reconnect and duplicate messages.
Test high-rate telemetry.
Test lifecycle cancellation.
Test low battery and degraded network conditions.
Test emergency-stop behavior.
Verify that AI-generated instructions cannot bypass the deterministic safety layer.

Conclusion

The resulting Kotlin layer can be extended with real ROS 2 bridges, NVIDIA Jetson services, computer vision models, smart-glasses SDKs, or multimodal AI backends. Keep hardware-specific code behind interfaces so the Android application remains maintainable as the robotics stack evolves.