# FleetOpt Verifiable Privacy (Python)

FleetOpt is a modular, open-source platform for privacy-preserving cross-fleet coordination of robotic workloads. This repository implements a production-ready MVP scaffold in Python, focusing on core data models, a contract-driven registry for aggregated signals, an asynchronous ADMM-like solver, offline delta synchronization, and secure governance/audit trails.

What you get in this MVP:
- Core data models: LocalRobotPlan, SharedSignals, PlanDelta, and PrivacyBudget.
- In-memory registry (GraphOfContracts) to exchange aggregated signals with simple policy blocks.
- A lightweight asynchronous ADMM-like solver coordinating two fleets with privacy budgets and dual variables.
- Privacy budget accounting and audit logging.
- Tiny ROS 2 adapter placeholder and TLS-configured transport scaffolding (ready to integrate with real ROS2 adapters).
- Tests validating cross-fleet optimization flow and privacy budgeting.

How to run tests
- Install dependencies (if any): this MVP uses only the standard library for tests, but you can install pytest if you wish to run externally.
- Run tests: `pytest -q`.
- Run packaging check: `python3 -m build`.

Architecture overview and how to contribute are described in AGENTS.md.