Task Allocation for Stochastic Multi-Agent Systems (TASMAS) under STL Specifications

Author: Zengjie Zhang (z.zhang3@tue.nl)

This project demonstrates the risk-aware allocation of real-time STL specifications for stochastic multi-agent systems, with restricted risk probabilities.

Introduction

Associated Research Work

This library is associated with the Arxiv article in https://arxiv.org/abs/2404.02111.

Relation with Existing Toolbox

The probstlpy library in this project is modified from the stlpy toolbox.

Scenario

This library considers a multi-bus routing scenario in a tourism attraction point to validate the effectiveness of the proposed method. The scenario contains four bus terminals denoted as T-X, where X$\in {A, B, C, D}$, four tourist gathering points denoted as GP-Z, where Z$\in {I, II, III, IV}$, and a single unloading point denoted as ULP (see the following Figure). Four buses, $A$ to $D$, are tasked with picking up tourists from the gathering points and transporting them to the unloading point. These buses initiate operations from their respective terminals and are expected to return when required. All buses are confined within the attraction point symbolized as ‘BOX’. The buses must avoid running into two buildings denoted as B-Y, with Y$\in {1, 2}$. When the tourists at a gathering point reach a certain number, a bus should be available to transport them to the unloading point within a tolerable time limit. Therefore, this study needs to handle dynamically allocated routing tasks, implying that new routing tasks may be assigned at any time $k \in {0, 1,· · · , N − 1}$.

Installation

System Requirements

Operating system

• Windows (compatible in general, succeed on 11)
• Linux (compatible in general, succeed on 20.04)
• MacOS (compatible in general, succeed on 13.4.1)

Python Environment

• Python version: test passed on python=3.11
• Required Packages:
  • matplotlib==3.9.0
  • numpy==1.26.4
  • scipy==1.14.0
  • treelib==1.7.0
  • control==0.10.0 (see Documentation)
  • gurobipy==11.0.2 (license required, see How to Get a Gurobi License)

Quick Guide

1. Install Python environment;

   pip install .
   

2. Activate the gurobi license (See How To). Note that this project is compatible with gurobi Released version 11.0.1. Keep your gurobi updated in case of incompatibility.

3. Running Instructions

   • Run the main script example/main.py;
   • Watch the terminal for runtime information;
   • The figures will show up at the end of running; They are also automatically saved in the root directory;
   • The figures may impede each other; Drag the figures for a better view;
   • Check out the logging file INFO.log for the runtime information.

Fine-Tuning the Code

Feel free to try out the code with different parameter settings in the src/utils/config.py file.

• Change the coordinates of the regions in this file to construct a different map;
• Change the standard deviation variable Sigma for different noise levels;
• Change the initial position of the robot in x0;
• Customize the lists of runtime specifications specs and their instants in times for various tasks.

License

This project is with a BSD-3 license, refer to LICENSE for details.