multi-agent-path-planning 0.1.0

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Multi-Agent path planning in Python

Introduction

This repository consists of the implementation of some multi-agent path-planning algorithms in Python. The following algorithms are currently implemented:

• Multi-Agent path planning in Python
  • Introduction
  • Dependencies
  • Centralized Solutions
    • Prioritized Safe-Interval Path Planning
      • Execution
      • Results
      • Reference
    • Conflict Based Search
      • Execution
      • Results
      • Reference
    • Post-Processing
      • Post-processing with TPG
  • Decentralized solutions
    • Velocity obstacles
      • Execution
      • Results
      • References
    • Nonlinear Model-Predictive Control
      • Execution
      • Results
      • References

Temporary Instructions for Running

Install poetry by running the following cmd (unix)

curl -sSL https://install.python-poetry.org | python3 -

Check install

poetry --version

If install isnt sourced, add to bash_rc (ubuntu) or bash_profile (mac), and source. (Clean this up if this is bad practice)

export PATH=$PATH:$HOME/.local/bin source ~/.bash_profile

Navigate to folder which contains pyproject.toml , and run

poetry install

poetry shell

Deactivate any other venvs including conda...

conda deactivate

Running

Run with the following,

sh scripts/lifelong_MAPF.sh

Dependencies

Install the necessary dependencies by running.

python3 -m pip install -r requirements.txt

Centralized Solutions

In these methods, it is the responsibility of the central planner to provide a plan to the robots.

Prioritized Safe-Interval Path Planning

SIPP is a local planner, using which, a collision-free plan can be generated, after considering the static and dynamic obstacles in the environment. In the case of multi-agent path planning, the other agents in the environment are considered as dynamic obstacles.

Execution

For SIPP multi-agent prioritized planning, run:

cd ./centralized/sipp
python3 multi_sipp.py input.yaml output.yaml

Results

To visualize the generated results

python3 visualize_sipp.py input.yaml output.yaml 

To record video

python3 visualize_sipp.py input.yaml output.yaml --video 'sipp.avi' --speed 1

Test 1 (Success)	Test 2 (Failure)

Reference

• SIPP: Safe Interval Path Planning for Dynamic Environments

Conflict Based Search

Conclict-Based Search (CBS), is a multi-agent global path planner.

Execution

Run:

cd ./centralized/cbs
python3 cbs.py input.yaml output.yaml

Results

To visualize the generated results:

python3 ../visualize.py input.yaml output.yaml

Test 1 (Success)	Test 2 (Success)

8x8 grid	32x32 grid

Reference

• Conflict-based search for optimal multi-agent pathfinding

Post-Processing

Post-processing with TPG

The plan, which is computed in discrete time, can be postprocessed to generate a plan-execution schedule, that takes care of the kinematic constraints as well as imperfections in plan execution.

This work is based on: Multi-Agent Path Finding with Kinematic Constraints

Once the plan is generated using CBS, please run the following to generate the plan-execution schedule:

cd ./centralized/scheduling
python3 minimize.py ../cbs/output.yaml real_schedule.yaml

Decentralized solutions

In this approach, it is the responsibility of each robot to find a feasible path. Each robot sees other robots as dynamic obstacles, and tries to compute a control velocity which would avoid collisions with these dynamic obstacles.

Velocity obstacles

Execution

cd ./decentralized
python3 decentralized.py -f velocity_obstacle/velocity_obstacle.avi -m velocity_obstacle

Results

• Test 1: The robot tries to stay at (5, 5), while avoiding collisions with the dynamic obstacles
• Test 2: The robot moves from (5, 0) to (5, 10), while avoiding obstacles

Test 1	Test 2

References

• The Hybrid Reciprocal Velocity Obstacle

Nonlinear Model-Predictive Control

Execution

cd ./decentralized
python3 decentralized.py -m nmpc

Results

• Test 1: The robot tries to stay at (5, 5), while avoiding collisions with the dynamic obstacles
• Test 2: The robot moves from (5, 0) to (5, 10), while avoiding obstacles

Test 1	Test 2

References

• Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance