Kotlin + WebSocket for NVIDIA Jetson Robot Telemetry

#kotlin #android #nvidia #robotics

Kotlin + WebSocket for NVIDIA Jetson Robot Telemetry

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 telemetry

@Serializable
data class JetsonTelemetry(
    val cpu: Float,
    val gpu: Float,
    val temperature: Float,
    val battery: Float
)

Step 2 — Create a WebSocket repository

class TelemetryRepository(
    private val socket: RobotSocket
) {
    fun telemetry(): Flow<JetsonTelemetry> =
        socket.messages()
            .map { Json.decodeFromString(it) }
}

Step 3 — Expose StateFlow

val telemetry = repository.telemetry()
    .stateIn(
        viewModelScope,
        SharingStarted.WhileSubscribed(5_000),
        JetsonTelemetry(0f, 0f, 0f, 0f)
    )

Step 4 — Display metrics

Text("GPU: ${state.gpu}%")
Text("CPU: ${state.cpu}%")
Text("Temperature: ${state.temperature}°C")

Step 5 — Reconnect safely

Use bounded retry delays and stop retrying when the ViewModel is destroyed.

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.