This repository provides the graph construction methods introduced in the paper “P3GASUS: Pre-Planned Path Execution Graphs for Multi-Agent Systems at Ultra-Large Scale.”

The core implementations are located in:

• discreteUtil.py — for discrete-space scenarios
• continuousUtil.py — for continuous-space scenarios

Example usage is demonstrated in the accompanying Jupyter notebooks:

• discrete.ipynb
• continuous.ipynb

The results/ folder contains detailed test outputs generated using:

• generateDiscreteResults.py
• generateContResults.py

The discrete implementation relies on lacam3’s Python bindings, generated from the repository:

➡️ https://github.com/Kei18/lacam3/tree/pybind

These bindings work with Python 3.11. For other Python versions, you may need to regenerate the bindings.

All methods implemented in discreteUtil.py — namely OriginalADG, SAGE, MAGE, and FORTED — inherit from the ExecutionGraph class.

Each ExecutionGraph instance contains:

• taskList
• graph (a NetworkX directed graph)
• robotList

See the paper for full algorithmic details.

Generates an open, obstacle-free world populated with agents while enforcing the requested amount of free space.

• freeSpacePercent ranges from 1–100.
• Returns:
  • startPositions
  • actions (all agents, all timesteps)
  • freeSpaceUsed

All experiments in the paper use ≥ 30% free space to ensure lacam3 can generate paths reliably.

Action encoding:

• 0: stay still
• 1: move East
• 2: move South
• 3: move West
• 4: move North

Runs the specified graph-construction method.

• filename — path to a .dat file for storing the DP matrix if the task list becomes extremely large (typically > 50,000 tasks).

Returns:

• graph.edges
• computation time

from discreteUtil import *

actions, starts, free = oneTestCase(100, 50)
testTime(OriginalADG, actions, starts)

To write a graph to disk:

exGraph = FORTED(actions, starts)
exGraph.fileWrite("Debug/")

The constructed execution graph is available as:

exGraph.graph

All methods implemented here — OriginalADG, SAGE, and MAGE — inherit from the common ExecutionGraph class.

Each ExecutionGraph instance contains:

• taskList
• graph (a NetworkX directed graph)
• robotList

The folder Continuous Scenario Paths/ contains sample path data in JSON format generated from MetaDrive

Converts path data loaded from JSON format into a NumPy matrix suitable for further processing.

Runs the specified continuous-space graph construction method.

• val — the method class (OriginalADG, SAGE, or MAGE)
• allPos — the positions matrix returned from jsonToNpy

Returns:

• communication length
• computation time

from continuousUtil import *

listOfMethods = [OriginalADG, SAGE, MAGE]

with open("Continuous Scenario Paths/10Agents_10fps", 'r') as f:
    try:
        data = json.load(f)
    except:
        print("Error loading JSON")

allPos = jsonToNpy(data, NUM_AGENTS=10)

for val in listOfMethods:
    commsLen, timeTaken = testTime(val, allPos)
    print(f"{val.__name__}: Time Taken - {timeTaken}, Comms Length - {commsLen}")

To write a graph to disk:

exGraph = SAGE(allPos)
exGraph.fileWrite("Debug/")

The constructed execution graph is available as:

exGraph.graph