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Flutter + NVIDIA Jetson: Building an Edge AI Robot Monitoring App

Flutter + NVIDIA Jetson: Building an Edge AI Robot Monitoring App

Introduction

Flutter is useful for operator dashboards, while Jetson performs edge AI and robotics workloads.

Architecture 

Robot
  |
Jetson
  |
AI / ROS 2
  |
FastAPI + WebSocket
  |
Flutter
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1. Create a FastAPI gateway 

from fastapi import FastAPI

app = FastAPI()

@app.get("/status")
def status():
    return {
        "robot": "online",
        "battery": 91,
        "ai": "running"
    }
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2. Create a Flutter API client 

class RobotApi {
  Future<Map<String, dynamic>> status() async {
    // GET /status
    return {};
  }
}
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3. Stream live events

Use WebSockets for events such as:

AI detection
Robot alert
Battery warning
Mission state
Camera status
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4. Build the dashboard

Suggested sections:

  • Robot status.
  • Live camera.
  • AI detections.
  • Mission controls.
  • Battery.
  • Diagnostics.

5. Add authentication

Use authenticated connections and never expose an unrestricted robot-control endpoint to the public internet.

6. Add offline handling

The app should distinguish:

Online
Connecting
Offline
Stale telemetry
Robot fault
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Conclusion

The combination of Jetson edge AI and Flutter creates a flexible architecture for remote robot monitoring while keeping latency-sensitive workloads close to the robot.

Useful Links

Website: www.v-modal.com

SDK Flutter: https://github.com/v-modal/vmodal_sdk_flutter

SDK Android: https://github.com/v-modal/vmodal_sdk_android

Discord: https://discord.gg/K72z28KUx

Reddit: https://www.reddit.com/r/v_modal/

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