Factory Bench (MRTA + MAPF)

A test bench for a heterogeneous multi-robot factory (semester project, Prof. Pranjal Vyas).

• MRTA (task allocation + scheduling): decides which robot carries which product, in which order.
• MAPF (multi-agent path finding): plans collision-free, time-stamped (x, y, t) routes on the factory map.
• The bench runs both together, feeds path delays back into the schedule, and replans when a robot fails or an aisle is blocked.
• Algorithms are swappable: built-ins, or your own Python file uploaded in the browser.

One app, two deliverables: the MRTA window (/mrta) and the MAPF window (/mapf) each show one module on its own; the main page shows both together.

Status (honest)

• Working and tested: engine (33 tests), 4 MRTA algorithms (greedy, auction, genetic, tabu), 3 MAPF algorithms (independent baseline, prioritized, CBS with fallback), events (blocked cells, robot failure/recovery), validators, in-browser Python (Pyodide), Run/Import/Compare tabs, module windows.
• The sample factory (scenarios/small_factory.json) uses made-up numbers. It is not from the professor.
• The Gemini import path (/api/convert) is written but not yet tested against the real API (no key during development). Without a key it answers 501 and the deterministic importers (.py, .json, ASCII grid) still work.
• The Sambhav prototype was never available, so the MRTA side is a fresh implementation of the same idea.
• Not decided yet: official metrics, the meaning of "Direct/Indirect" in the notes, final deliverable wording.

Layout

python/bench/core      grid, scenario, tasks, schedule, validators, orchestrator, plug-in loader
python/bench/mrta      greedy, auction, genetic, tabu
python/bench/mapf      lowlevel (space-time A*), independent, prioritized, cbs
python/tests           pytest suite
scenarios/             sample factory + events
plugins-examples/      templates to copy for your own MRTA / MAPF algorithm
web/                   Next.js app (UI, Gemini route, Pyodide worker)

Run locally

cd python && python3 -m pip install pytest && python3 -m pytest -q   # engine tests
cd ../web && npm install && cp .env.example .env.local
npm run dev                                                          # http://localhost:3000

npm run dev/build first copies the Python engine and the Pyodide runtime into web/public/ (git-ignored), so the browser loads everything from your own domain.

Environment variables (set in Vercel, never in the repo)

name	purpose
GEMINI_API_KEY	your Gemini key (server only)
GEMINI_MODEL	model id you want to use, e.g. the one shown in Google AI Studio
APP_ACCESS_CODE	optional; if set, converting uploads needs this code

Deploy on Vercel

1. Vercel dashboard > Add New > Project > import this GitHub repo.
2. Root Directory: web. Framework: Next.js (auto-detected).
3. Add the environment variables above, then Deploy.

Your own algorithm

Copy a file from plugins-examples/, edit it, upload it in Import. One file = an INFO dict + allocate(...) (MRTA) or plan(...) (MAPF). Uploaded code runs only in a Web Worker in the visitor's browser with network access blocked by the page's CSP, under a soft time limit and a hard stop button.

Model assumptions (to confirm)

Whole time steps; a robot with step cost k needs k steps per cell; conflicts = same cell at the same time, or swapping cells (following is allowed); a robot with no work left leaves the floor; a failed robot stays as an obstacle.