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Problem: In dynamic, multi-robot environments (warehouses, field robotics, drone swarms), coordinating task assignments in a way that is scalable, auditable, and robust to intermittent connectivity is hard. Centralized schedulers lose privacy, strugg
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README.md

Algebraic Auction Studio for Robotic Fleet (AAS-RF)

This repository implements a production-grade MVP of the Algebraic Auction Studio for robotic fleets.

  • DSL to declare agents (robots), tasks, budgets, and preferences
  • Compositional optimization engine (ADMM-lite) for distributed fleet allocation
  • Offline-first runtime with delta-sync and tamper-evident audit logs
  • CatOpt-inspired data contracts and adapters registry for heterogeneous platforms
  • Governance, privacy budgeting, and an adapters marketplace scaffold
  • Phase-driven MVP plan with HIL testing capabilities

This package is structured as a Python project under the package name algebraic_auction_studio_for_robotic_fle. See the AGENTS.md for architecture details and testing commands.

Usage: see tests under tests/ for examples and run bash test.sh to verify CI-like flows locally.

Licensing: MIT (placeholder; update as needed)

""" Note: This README is intentionally concise to keep the repo developer-focused. A more detailed marketing/tech readme should accompany a real release. """