Building a Kotlin Android Camera Viewer for ROS 2 Robots

#android #kotlin #robotics #ros2

Building a Kotlin Android Camera Viewer for ROS 2 Robots

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 camera frames

data class RobotFrame(
    val data: ByteArray,
    val timestamp: Long
)

Step 2 — Create a bounded stream

val frames = Channel<RobotFrame>(
    capacity = 2,
    onBufferOverflow = BufferOverflow.DROP_OLDEST
)

Step 3 — Receive frames

fun onFrame(data: ByteArray) {
    frames.trySend(
        RobotFrame(data, System.currentTimeMillis())
    )
}

Step 4 — Process asynchronously

viewModelScope.launch(Dispatchers.Default) {
    for (frame in frames) {
        processFrame(frame)
    }
}

Step 5 — Display the stream

Use an Android-compatible video/image rendering component appropriate to the transport. Keep decoding away from the main thread.

Step 6 — Measure latency

Track capture, network receive, decode, and display timestamps independently.

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.