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.
• Lightweight DSL seeds for LocalRobotPlan/SharedSignals/PlanDelta (core/dsl.py)
• Toy adapters for interoperability (adapters/ros2_adapter.py and adapters/gazebo_adapter.py)

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.

Extending and publishing

• This repo is designed to be extended in a production-ready manner. See READY_TO_PUBLISH for publishing flag.
• A minimal DSL seed (core/dsl_seed.py) and an MVP contract seed (core/dsl.py) help bootstrap interoperability with adapters.
• The two adapters (adapters/ros2_adapter.py and adapters/gazebo_adapter.py) are placeholders to be wired to real ROS 2 and Gazebo integrations.

Contributing

• Follow the architecture described in AGENTS.md and keep changes cohesive and well-tested.

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