Project TERRAIN seeks independent review for offline navigation simulation

Hello Open Robotics community,

I’d like to introduce Project TERRAIN, an independent robotics research project exploring navigation through uncertain environments, fault tolerance, and simulated safety-stop behavior.

The project includes a working offline Python simulation with:

• Deterministic navigation and reproducible test scenarios
• Limited-observation exploration
• Communication-loss and emergency-stop scenarios
• Unauthorized-command handling
• Automated regression testing

I’m seeking constructive technical feedback, particularly on the navigation assumptions, fault-injection tests, and opportunities to improve the simulation.

The project is conceptual and simulation-based. It does not demonstrate physical robot safety or hardware readiness, and it is not affiliated with Tesla or Open Robotics.

GitHub repository:

Repository icartpluk/project-terrain

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Engineering review discussion:

Open issue icartpluk/project-terrain

Engineering review requested: Project TERRAIN offline robotics simulation

opened 03:53PM - 01 Nov 26 UTC

icartpluk

## Request for independent technical review Project TERRAIN is an independent, …public, **inert offline grid-simulation** and conceptual robotics research project. We welcome constructive review from robotics, autonomy, simulation, safety engineering, and cybersecurity practitioners. **Repository:** https://github.com/icartpluk/project-terrain ### How to run - Python 3.11+; no third-party packages required. - From the repository root, run `python simulation/start.py` (or use `START-DEMO.bat` on Windows after extracting the repository). - For regression tests, run `python -m unittest discover -s simulation -v`. - See [simulation/README.md](../blob/main/simulation/README.md) for the replay controls, seeded maps, and limitations. ### Review questions 1. Are the navigation and limited-observation assumptions clear and appropriately bounded? 2. Are the deterministic fault-injection tests and stop-state invariants adequate for the *simulated* model? What important edge cases are missing? 3. Are the Python/browser replay parity and malformed-map-input checks convincing and reproducible? 4. Are safety and cybersecurity requirements clearly separated from demonstrated capabilities? 5. What would make this project more useful as a general-purpose educational or research simulation? ### Scope and caveats The simulator uses abstract cells and ticks, not real sensors, braking physics, actuator controls, or hardware E-stop. It is **not** a Tesla-affiliated project, a certified safety system, or a demonstrated hazardous-material recovery capability. No physical or hazardous operations are requested. See [development status](../blob/main/docs/development-status.md) and [safety validation gates](../blob/main/docs/safety-validation-gates.md). Please leave specific observations, reproducible failures, and suggestions in this issue. Independent reviewers are welcome; feedback does not imply endorsement.

I’d appreciate feedback from anyone with experience in robotics navigation, simulation, or safety engineering.

Thank you!