Building a Robot Diagnostics Dashboard with Kotlin and Compose

#android #kotlin #ai #robotics

Building a Robot Diagnostics Dashboard with Kotlin and Compose

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 diagnostics

data class Diagnostics(
    val connected: Boolean,
    val battery: Float,
    val temperature: Float,
    val cpu: Float,
    val gpu: Float,
    val lastMessageMs: Long
)

Step 2 — Expose diagnostics through StateFlow

private val _diagnostics =
    MutableStateFlow(Diagnostics(false, 0f, 0f, 0f, 0f, 0))

Step 3 — Create Compose metric cards

@Composable
fun DiagnosticCard(title: String, value: String) {
    Card {
        Column(Modifier.padding(12.dp)) {
            Text(title)
            Text(value)
        }
    }
}

Step 4 — Detect stale telemetry

val stale =
    System.currentTimeMillis() - state.lastMessageMs > 3000

Step 5 — Add logs

Store structured events such as connection changes, model failures, sensor errors, and watchdog activations.

Step 6 — Add export

For production systems, allow diagnostics to be exported for support and debugging, while avoiding sensitive information.

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.