I am prototyping **EdgeAI Forge**, a local-first engineering architecture intended to help generate, test, benchmark, and document ROS 2 and machine-vision pipelines.
The initial target workflow is a ROS 2 vision pipeline generator. Given a requirement such as:
> Read frames from a USB camera, run object detection, publish detections, expose health status, benchmark performance, and prepare deployment to Jetson.
the system should eventually produce reviewed artifacts including:
- package manifests and directory structure;
- Python or C++ nodes;
- topics, messages, services, and actions;
- parameters and launch files;
- unit, integration, and launch tests;
- simulation or recorded-data validation;
- Docker/Jetson packaging;
- FPS and latency benchmarks; and
- assumptions, limitations, and operating documentation.
The current proof of concept contains Planner, Vision, and ROS prompt agents backed by a local Ollama endpoint. It generates design output, not production-ready ROS packages. A broader scaffold contains API, dashboard, infrastructure, and observability components.
The safety boundary is important: generated robot-motion or production-deployment artifacts should never run automatically. The intended sequence includes simulation, test evidence, human review, explicit approval, logging, and rollback.
I would appreciate feedback from the ROS community on:
1. Which package archetype would make the best first supported template?
2. How should generated packages be evaluated beyond compilation and linting?
3. Which launch_testing, rosbag, Gazebo, or other simulation patterns should be mandatory?
4. How should the generator encode QoS, lifecycle nodes, diagnostics, and hardware assumptions?
5. What safeguards would make this useful without encouraging unsafe deployment practices?
Project: GitHub - afridali123/EdgeAI_Forge · GitHub
Background: EdgeAI Forge: My Journey Toward a Local Agentic AI Platform for Industrial Automation
The project is early, and I am sharing it to collect design criticism before implementing the deeper ROS workflow.