Kotlin + ROS 2 Navigation Dashboard

#android #kotlin #robotics #ros2

Kotlin + ROS 2 Navigation Dashboard

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 navigation state

data class NavigationState(
    val connected: Boolean = false,
    val goalX: Double = 0.0,
    val goalY: Double = 0.0,
    val status: String = "Idle"
)

Step 2 — Expose it with StateFlow

class NavigationViewModel : ViewModel() {
    private val _state = MutableStateFlow(NavigationState())
    val state = _state.asStateFlow()
}

Step 3 — Send a navigation goal

fun sendGoal(x: Double, y: Double) {
    viewModelScope.launch(Dispatchers.IO) {
        rosBridge.sendGoal(x, y)
    }
}

Step 4 — Display the goal

@Composable
fun NavigationStatus(state: NavigationState) {
    Column {
        Text("Status: ${state.status}")
        Text("Goal: ${state.goalX}, ${state.goalY}")
    }
}

Step 5 — Add safety

Show the robot's connection, localization state, and navigation status before allowing a new goal. Provide an explicit cancel/stop action.

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.