Kotlin + MQTT for Real-Time Robot Fleet Management

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 fleet telemetry

@Serializable
data class RobotTelemetry(
    val robotId: String,
    val battery: Float,
    val status: String
)

Step 2 — Connect with MQTT

Use an MQTT client supported by your Android project and configure TLS authentication for production.

fun onTelemetry(payload: String) {
    val telemetry =
        Json.decodeFromString<RobotTelemetry>(payload)
}

Step 3 — Maintain fleet state

private val _robots =
    MutableStateFlow<Map<String, RobotTelemetry>>(emptyMap())

Step 4 — Update one robot

_robots.update { current ->
    current + (telemetry.robotId to telemetry)
}

Step 5 — Display the fleet

LazyColumn {
    items(robots.values.toList()) {
        Text("${it.robotId}: ${it.status}")
    }
}

Step 6 — Handle offline robots

Record the last-seen timestamp and visually distinguish stale telemetry from live data.

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.